人工智能 | SCI期刊专刊信息6条

人工智能

Computer Speech and Language

Special Issue on Speech Dementia

全文截稿: 2019-06-15

影响因子: 1.776

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：人工智能 - 4区

网址: http://www.journals.elsevier.com/computer-speech-and-language

This is a call for papers for a Special Issue onSpeech ＆ Dementia Automatic Screening for Dementia from Spoken Communication, to be published in early 2020 inComputer Speech and Language, an official publication of the International Speech Communication Association.

Dementia is an incurable progressive disease that ranks first among the age-related fears of people aged 60+ years and affects about 50 million people worldwide, a number that is estimated to double every 20 years. In 2018, costs exceeded the $1 trillion USD mark, with 90% incurred in the high-income countries. While no preventive measures nor curative therapeutic interventions for dementia are known yet, studies show that early interventions can delay the progression of the disease. Thus, it is pivotal to recognize symptoms as early as possible. Unfortunately, current diagnostic procedures require a thorough examination by medical specialists, which are too cost- and time-consuming to be provided frequently on a large scale.

Spoken language skills are well established early indicators of cognitive abilities. Since speech is the most important means of communication used on a daily basis, monitoring of relevant indicators offers great potential for easy-to-use casual testing. Recently, assessment systems based on automatic speech processing methods have been developed which automatically extract relevant acoustic and linguistic features from spoken conversations, in order to interpret signs of cognitive decline and thus supporting clinicians in the diagnosis of dementia. Such systems could improve current diagnostic practice by providing easy-to-use, low-cost means of detecting and tracking early signs of dementia, which currently cannot be offered due to cost, time, and lack of human resources.

The special issue onSpeech and Dementiawill bring together researchers from the fields of speech and language processing, medicine, psychology, as well as disciplines related to health and aging, and thus will contribute to the advancement of cross-disciplinary speech and language research.

Topics of interest include (but are not limited to):

Speech or language resources for detection and tracking of dementia

Speech and language related features for cognitive assessment (e.g. MCI, dementia)

Detection of early signs of dementia from speech and language data

Longitudinal tracking of dementia

User-evaluation and field trials of dementia detection

Methods, algorithms and tools for detection and tracking of dementia

Spoken communication systems for monitoring, assisting or activating people with dementia

人工智能

Computer Speech and Language

Special Issue on the 7th Dialog System Technology Challenge 2019

全文截稿: 2019-07-01

影响因子: 1.776

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：人工智能 - 4区

网址: http://www.journals.elsevier.com/computer-speech-and-language

The 7th Dialog System Technology Challenge(DSTC7) focuses on how to apply End-to-End technologies to Dialog Systems in a pragmatic way. DSTC7 consists of the following three tracks:

(1) NOESIS: Noetic End-to-End Response Selection Challenge

(2) End-to-End Conversation Modeling: Moving beyond Chitchat

(3) Video Scene-Aware Dialog Track in DSTC7

For each track, we have released data and evaluation tools, as well as baselines consisting of training tools and models. For all tracks, the test data will be provided on Sep. 10th, the final system outputs will be submitted by Oct. 8th, and the human evaluation results by Oct 23rd. Then we will have two wrap-up workshops, giving the scientific community the opportunity of reviewing the state-of-the-art performance and novel approaches, as well as discussing the next directions for dialog technology challenges. For more information, seehttp://workshop.colips.org/dstc7/index.html

This special issue will host work on any of the three DSTC7 tasks. We anticipate most papers will describe DSTC7 entries, and we particularly welcome papers describing novel techniques that advance the state-of-the-art in dialog system technologies. Papers may describe entries in the official DSTC7 challenge, or work on DSTC7 data but outside or after the official challenge. We also welcome papers that analyze the DSTC7 tasks or results themselves. Finally, we also invite papers from participants of previous DSTC editions, as well as general technical papers oriented to End-to-End dialog technologies (e.g. conversational agents, dialog breakdown detection, reasoning and understanding, automatic dialog evaluation, or dialog policies and tracking, among others).

人工智能

Applied Soft Computing

Special Issue on Data-driven Decision Making - Theory, Methods, and Applications

全文截稿: 2019-08-15

影响因子: 3.907

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：人工智能 - 2区

  • 小类 : 计算机：跨学科应用 - 2区

网址: http://www.journals.elsevier.com/applied-soft-computing/

Data-driven decision making approaches have been widely used in emergency response, medicine, manufacturing, renewable energy, and so on. Businesses generally use a wide range of tools to get useful information from big data, and to present it in ways that back up decisions. They form a hot research topic owing to their importance and effectiveness in addressing aspects of uncertainty and incompleteness of data. Moreover, in some cases the incomplete information about the consequences of the alternatives can be tackled by means of the theory of Soft Computing (SC), Data Mining (DM) and Artificial Intelligence (AI). Now and in the future, data-driven decision making under uncertainty and incompleteness would be a quite promising research line representing a high quality breakthrough in this topic.

The objective of this special issue is to explore latest up-to-date methods of SC, DM and AI, and their applications in data-driven decision making under uncertainty and incompleteness environment. Both theoretical and applied results with applications are sought for. It offers a concentrative venue for researchers to make rapid exchange of ideas and original research findings in data-driven decision making problems. In particular, new interdisciplinary approaches in SC, DM and AI for decision making theories and applications, or strong conceptual foundation in newly evolving topics are especially welcome.

TOPICS OF INTEREST FOR THE SPECIAL ISSUE

We invite researchers and experts worldwide to submit high-quality original research papers and critical survey articles on the following potential topics and their applications, but are not limited to:

Big (small) data analysis for decision making

Soft Computing and its applications in decision making

Nature-inspired optimization for decision making

Fuzzy Multiple Criteria/Objective Decision Making

Consensus and cooperation for decision making

Rough Sets and its applications

Fuzzy set qualitative comparative analysis

Decision making with incomplete / uncertain systems

Other DM, AI for decision support/making

人工智能

Artificial Intelligence in Medicine

Medical Analytics for Healthcare Intelligence

全文截稿: 2019-08-31

影响因子: 2.879

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：人工智能 - 3区

  • 小类 : 工程：生物医学 - 3区

  • 小类 : 医学：信息 - 3区

网址: http://www.journals.elsevier.com/artificial-intelligence-in-medicine/

With rapid advancement in technology, the healthcare industry is producing and collecting data at a staggering speed. Vast amount of healthcare data has been collected through sources such as genomics, electronic health records, medical monitoring devices and health-related mobile phone apps, which result in a mixture of structured data such as patient demographics and medication list as well as semi-structured or unstructured data such as doctor notes and medical images. However, raw data is barely of direct interest to healthcare stakeholders unless potentially useful knowledge is extracted.

The advancement of data analytics facilitates the generation of data-driven models to improve the understanding of disease mechanisms, increase the efficiency in healthcare delivery, reduce overall cost to the healthcare systems and facilitate clinical decision support. The use of analytics in the ever-increasing quantity of healthcare data presents rich opportunities, but also a number of daunting challenges such as the vast amount of unstructured data, the concern of privacy and security issue, the lack of data standardization, the issues of data storage and transfers. These challenges have slowed the process of leveraging healthcare data, which leads to the deployment of analytics models in healthcare not convincingly demonstrated due to the rarity of their application.

To embrace the challenges and opportunities in designing and deploying intelligent healthcare systems, this special issue aims to encourage submissions of scientific findings from both academia and healthcare industry that present the fundamental theory, techniques, applications and practical experiences in the context of designing, implementing or evaluating analytics for healthcare intelligence.

The topics of this special issue include, but are not limited to:

Data Mining and Knowledge Discovery in Healthcare

Medical Expert Systems

Ontologies in Healthcare

Machine Learning in Healthcare

Clinical Decision Support Systems

Text Mining and Natural Language Processing in Medical Documents

Medical Imaging

Deep Learning Applications in Healthcare

Predictive Modelling for Personalized Treatment

Medical Recommender Systems

Intelligent Systems for Electronic Health Records

Computational Intelligence for Healthcare

Intelligent Medical Devices and Sensors

Visual Analytics for Healthcare

Computer-aided Diagnosis

Modelling and Reasoning with Time in Medical Data and Systems

人工智能

Artificial Intelligence in Medicine

Trusted virtual Environments for Neuro-Informatics

全文截稿: 2019-09-30

影响因子: 2.879

CCF分类: C类

中科院JCR分区:

  • 大类 : 工程技术 - 3区

  • 小类 : 计算机：人工智能 - 3区

  • 小类 : 工程：生物医学 - 3区

  • 小类 : 医学：信息 - 3区

网址: http://www.journals.elsevier.com/artificial-intelligence-in-medicine/

There are many new technologies and applications that advanced our daily lives; the listed technologies: IoT, hyperscale computing and multimedia. The approaches in virtual environments and its fast expansion facilitate the access to digital contents, images, and videos at any time and from anywhere in this smart digital environments. The virtual environments will play an increasingly important role in a future of smart environment based on Artificial Intelligence and IoT. While machine-to-machine communications can be secured by existing information and cyber security tools, the interaction between humans and automated systems is more challenging to establish trust due to varied human factors. The integration between these new technologies and multimedia applications built our life accessible in many aspects, but, meanwhile, it carry up serious challenges including attaining all these large amounts of digital contents in digital strained environments with limited storage and processing capabilities such as smart devices. Further, computerized systems and automation from separated intelligence systems may also need media to connect and thus have challenge in building mutual trust via such multimedia.

The trust concern is not limited to the physical and online services, but also future virtual and augmented reality environments. Potential topics included, but not limited

Neuro and brain imaging (EEG, fMRI, PET, CT, other)

Multiple modalities for brain activities (memory, learning, decision-making, emotion, consciousness other)

Neuroinformatics platform for management, federation, sharing and analysis of multi-dimensional neuroscience data

3D Visualization, modeling and animation techniques for neuro-informatics

Deep learning and learning of representations in artificial systems for virtual environments

Machine learning analyses using large-scale neuroimaging data

Machine learning analyses combining neuroimaging and genetics

Neuro-cognitive virtual environment for children with Autism

人工智能

Applied Soft Computing

Special Issue on Soft Computing for Network and System Security of Internet of Everything

全文截稿: 2019-12-31

影响因子: 3.907

CCF分类: 无

中科院JCR分区:

  • 大类 : 工程技术 - 2区

  • 小类 : 计算机：人工智能 - 2区

  • 小类 : 计算机：跨学科应用 - 2区

网址: http://www.journals.elsevier.com/applied-soft-computing/

Special Issue on Soft Computing for Network and System Security of Internet of Everything

The Internet of Everything (IoE) binds together people, objects, processes, data, applications, and services to make networked connections more relevant and valuable than ever before. However, network and system security technologies are required to make these IoE based infrastructure, services, and contents more secure and reliable. To deal with the growing IoE based on network and system security, it is necessarily required to apply the soft computing approaches using security combined technologies such as artificial neural networks based on security, big data processing with security, fault tolerant system to secure IoE systems. The IoE enabled with the soft computing based security aims to include all sorts of secure and reliable connections that one can envision, thereby covering other similar concepts with security requirements and countermeasure. Unlike other similar concepts, it produces not only physical measurement, but also virtual/cyber sensory data and continues to extend the traditional M2M/IoT/IIoT/WoT by providing secure and reliable connectivity and interaction between the physical and cyber worlds. In order to connect physical and cyber world using various smart applications and services, the soft computing based security in the IoE encompasses a number of technological security components such as cryptography, privacy protection, encryption/decryption, hash, intrusion detection, firewall, even block chain, these days.

In this context, this special issue focuses on the state-of-the-art technologies on soft computing to deal with network and system security of IoE systems. Therefore, this issue covers various research challenges for a wide area of technological components of soft computing based secure and reliable IoE system such as cyber physical system security, virtual connectivity security, cloud computing security, big data security and industrial application security. The topics include but are not limited to:

- Soft computing-based security modeling for IoE architecture

- Soft computing-based big data security for IoE system

- Fog/edge/cloud/distributed computing security for IoE system

- Security issues in machine learning and deep learning for IoE system

- Intelligent industrial control system and network security for IoE system

- Intelligent sensors, connectivity, and platform security technologies for IoE system

- Intelligent privacy enhanced systems and applications for IoE system

- Soft computing for the integration of cryptography in IoE System

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