【今日新增】中等难度SCI期刊截稿信息9条

全文截稿: 2017-07-01

影响因子: 1.538

期刊难度: ★★★

CCF分类: C类

网址: http://www.springer.com/journal/11036/about

Along with the fast developing of modern communication and signal processing technologies, the amount of high quality wireless services is required and increasing exponentially. According to the prediction of Cisco VNI Mobile Forecast 2016, Global mobile data traffic will increase nearly eightfold between 2015 and 2020, and mobile network connection speeds will increase more than threefold by 2020. Hence, there are still big gap between the future requirements and current communications, networking, and signal processing technologies. In this view, we are aiming to discuss the potential advantages of adopting some intelligent algorithms into the next generation communication systems. As an emerging discipline, machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence, and explores the study and construction of algorithms that can learn from and make predictions on complicated scenarios. We hope the integrating of machine learning algorithms and other intelligent algorithms into communication systems will improve the quality of service and make the systems smart, intelligent, and efficient. We invite high quality original research papers describing recent and expected challenges or discoveries along with potential intelligent solutions for future wireless communications and networks. Both original, unpublished contributions and survey/tutorial types of articles are encouraged.

Topics of interest include, but are not limited to, the following scope:
- Machine learning - Intelligent positioning and navigation
- Intelligent Multimedia Processing and Security
- Intelligent Wireless Networks and Security
- Cognitive Radio and Intelligent Networking - Intelligent Internet of Things
- Intelligent Satellite Communications - Intelligent Image Processing
- Green Communications - Intelligent Ad-hoc Sensor Networks
- Resource Allocation in Wireless and Cloud Networks
- Intelligent Radar Signal Processing
- Network Coding and Cooperative Communications
- Advanced Signal Processing

全文截稿: 2017-07-14

影响因子: 1.272

期刊难度: ★★★

CCF分类: C类

网址: http://www.springer.com/journal/138/about

High-throughput imaging technologies have enabled researchers and practitioners to acquire large volumes of biomedical images automatically everyday. This has made it possible to conduct large-scale, image-based experiments for biomedical discovery. The main challenge and bottleneck in such experiments is the conversion of "biomedical big data" into interpretable information and hence discoveries. Computer vision has huge potential for automated analysis and understanding of such data, including image segmentation, object detection, shape analysis, object tracking, event detection, and computer-aided diagnosis. Not only do computers have more "stamina" than human annotators for such tasks, they also perform analysis that is more reproducible and less subjective. Recent years, novel machine learning techniques, especially deep learning, have revolutionized multiple areas in computer vision and significantly advanced the state-of-art.

This special issue serves to attract active researchers around the world to share their recent innovation in this exciting area. We solicit original contributions in three-fold: (1) present state-of-the-art theories and novel applications in biomedical big data analysis; (2) survey the recent progress in this area; and (3) build benchmark datasets. The list of possible topics includes, but not limited to:

1.Biomedical Big Data Representation
- Hand-crafted/data-driven feature learning
- Large-scale multimodal biomedical data acquisition
- Novel dataset and benchmark for biomedical big data analysis

2.Biomedical Big Data Learning
- Biomedical big data organization, retrieval and indexing
- Time-series modeling
- Multimodal information fusion

3.Biomedical Big Data Understanding and Applications
- Image restoration
- Image segmentation
- Image Registration
- Object detection & tracking
- Event modeling and localization
- Health, economics, and other applications involving biomedical big data

全文截稿: 2017-07-15

影响因子: 1.538

期刊难度: ★★★

CCF分类: C类

网址: http://www.springer.com/journal/11036/about

As we are striding into the initial era of the Internet of Things (IoT), a key question is how we make the most of IoT for all stakeholders, including platform providers, IoT application developers, end-users, large and small organizations (such as city councils, enterprises) that wish to provide better services, and manufacturers of smart devices. The amount of smart devices immersed in everyday life, from manufacturing to clothing, is growing every day in terms of power, processing and network connectivity. The sheer size and variety of contextual data that they produce, along with the actions they can take on their environment, is enormous. It remains to be answered how all this potential will come to bear; this special issue focuses on the discussion on the challenges posed by these trends.

The "Everything as a Service" deployment paradigm will enable the easy adoption of IoT based services and applications by end users, while forcing providers of smart objects and middleware platforms to architect their solutions accordingly. To maximize impact and adoption, the barrier-to-entry should be lowered by making development of new applications and the ingestion and exposure of smart objects as easy as possible. Original submissions, not under any concurrent reviews, are solicited in all areas related to advances on applications, methods and approaches envisioning to address the new challenges in the IoT as a service.


Topics of interest include, but are not limited to, the following scope:
- Smart objects as a service
- IoT marketplace-for offering IoT based applications and services
- API economy - for easier and tighter integration
- Semantic Web technologies for IoT: registry, storage
- IoT delivery platforms - existing and emerging platform and architectures for exposing and interacting with IoT
- Federated IoT support
- Non Functional Requirements (NFR) for IoT - security, scalability, responsiveness and more
- Standardization - standard areas needed
- Business models
- IoT DevOps
- IoT context based analytics
- IoT application deployment success stories
- Mobile First IoT: mobile backend as a services (MBaaS) and smartphone as data prosumers

全文截稿: 2017-09-30

影响因子: 1.0

期刊难度: ★★★

CCF分类: C类

网址: http://www.springer.com/engineering/signals/journal/12083

Coverage problem has been a hot topic in the last two decades with the rising of wireless sensor networks. It is concerned with the quality of sensing about the targets of interest that the sensor network is deployed for. Extensive previous effort has been devoted to exploring coverage theory, which ranges from point coverage, barrier coverage to area coverage in line with the dimension of concerned targets. Though these theoretical investigations provide insightful understanding about coverage, there are quite limited previous studies reporting coverage application in practical scenarios. How much can we gain by applying theoretical coverage results from the practical point of view? Further, some recent results are provided about coverage problem in other research fields, such as cellular network and social networks. Is it possible to provide a general theoretical framework for network coverage given the understanding gained from sensor networks? The answers to these two fundamental questions would extend the boundaries of traditional sensor coverage and push the coverage research from theory to practice.

The objective of this call is to bring latest thoughts and applications in generic network coverage, that help put together a clear map for this important area. Papers addressing one or more of the topics below are of particular interest:
- Sensor deployment for coverage in sensor networks
- Sensor scheduling for coverage in randomly deployed sensor networks
- Coverage modelling in sensor networks
- Dynamic coverage in mobile sensor networks
- Joint optimization of coverage and connectivity
- Practical applications in sensor networks
- Coverage problem in varying fields
- Practical implementations in varying fields
- Studies on the framework of generic network coverage
- Optimization for generic network coverage

全文截稿: 2017-10-23

影响因子: 2.392

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/neurocomputing/

With the wide application of machine learning algorithms to the real world, class imbalance and concept drift have become crucial learning issues. Applications in various domains such as risk management, anomaly detection, fraud detection, software engineering, social media mining, and recommender systems are affected by both class imbalance and concept drift. Class imbalance happens when the data categories are not equally represented, i.e., at least one category is minority compared to other categories. It can cause learning bias towards the majority class and poor generalization. Concept drift is a change in the underlying distribution of the problem, and is a significant issue specially when learning from data streams. It requires learners to be adaptive to dynamic changes.

Class imbalance and concept drift can significantly hinder predictive performance, and the problem becomes particularly challenging when they occur simultaneously. This challenge arises from the fact that one problem can affect the treatment of the other. For example, drift detection algorithms based on the traditional classification error may be sensitive to the imbalanced degree and become less effective; and class imbalance techniques need to be adaptive to changing imbalance rates, otherwise the class receiving the preferential treatment may not be the correct minority class at the current moment. Therefore, the mutual effect of class imbalance and concept drift should be considered during algorithm design.

The aim of this special issue is to bring together the original work from the areas of class imbalance learning and concept drift in order to solve the combined issue of class imbalance and concept drift. In order to advance the state-of-the-art on the combined issue, it is important to also advance the state-of-the art in each individual area. Therefore, this special issue encourages submissions not only on the combined issue, but also on these two areas themselves.

The list of possible topics includes, but is not limited to:

(1)Research topics related to the combined issues of class imbalance and concept drift:
- Concept drift detection in imbalanced data streams.
- New data-level and algorithm-level approaches to dealing with class imbalance in non-stationary environments.
- Semi-supervised learning and active learning approaches to dealing with imbalanced data streams.
- Adaptive ensemble approaches for imbalanced data streams.
- Performance evaluation on imbalanced data streams in incremental and online learning scenarios.
- Case studies and real-world applications dealing with both class imbalance and concept drift.

(2)Research topics related to class imbalanced learning:
- Data-level and algorithm-level techniques for imbalanced data.
- Ensemble learning approaches for imbalanced data.
- Cost-sensitive and cost-free learning approaches.
- Imbalanced data with multiple classes or multiple labels.
- Semi-supervised class imbalance learning.
- Case studies and real-world applications dealing with class imbalanced data.

(3)Research topics related to learning in the presence of concept drift:
- Passive and active approaches to dealing with concept drift.
- Concept drift detection methods.
- Chunk-based and online learning approaches for non-stationary environments.
- Approaches to dealing with recurring concepts.
- Adaptive ensemble approaches.
- Semi-supervised learning in non-stationary environments.
- Case studies and real-world applications involving concept drift.

全文截稿: 2017-10-29

影响因子: 1.538

期刊难度: ★★★

CCF分类: C类

网址: http://www.springer.com/journal/11036/about

This special issue aims to bring together researchers interested in formulating and implementing innovative solutions under big data and cloud technologies for mobile networks. Data analytics, trust management, massive data management are few of the well established research areas which has gained increased importance over the past years. The technological advancements enrich the creativity of researchers which helps in attaining efficient adaptive solutions. Such rich adaptive solutions open the way to new dimensions of security violations in mobile networks. Hence, the focus of this special issue is to the interest of interdisciplinary target audience spanning: any recent challenge applicable for mobile networks.

Topics of interest include, but are not limited to, the following scope:
- Mobile cloud platforms
- Integration solutions for mobile, cloud and big data infrastructures
- Management of massive data using mobile clouds
- Resource management and scheduling in mobile clouds
- New programming models for mobile networks
- Adaptive software for big data in mobile networks
- Resilience issues in mobile clouds
- Security challenges in mobile cloud environment
- Next generation mobile computing services and applications
- Migration mechanisms in mobile cloud computing environment
- Network framework design for mobile cloud service requirements
- Application of mobile computing based solutions in cloud and big data environments
- Mobile system software enhancements
- Big data applications in mobile clouds
- Collaborative infrastructures for analytics in mobile networks
- Protocols and emerging standards for mobile networks in cloud and big data environment
- Trust management challenges in mobile cloud
- Resource optimization in mobile cloud environment

全文截稿: 2017-10-31

影响因子: 1.586

期刊难度: ★★★

CCF分类: C类

网址: http://www.journals.elsevier.com/pattern-recognition-letters/

Handwriting analysis and recognition has been widely studied for many years contributing to the development of a research field, which produced a large amount of both theoretical and experimental results. In this framework, the automatic processing of handwriting and drawing features, both on-line and off-line, in order to automatically classify specimens of handwriting, represents the core information processing technology behind many successful applications that are in daily use. Examples of the such applications can be found in human-machine interfaces, such as the electronic pen pad and automatic signature verification equipment, mail sorting, check reading and form processing, just to mention a few. The term graphonomics, coined in 1982, intends to capture the multidisciplinary and interdisciplinary nature of the entire research field. It denotes the scientific and technological effort involved in identifying relationships among the planning and generation of handwriting and drawing movements, the resulting spatial traces of writing and drawing instruments (either conventional or electronic), and the dynamic features of these traces. Even if many effective methods have been proposed in the literature and successfully applied in a number of real applications, these problems are still very far from being solved in the general case.

The aim of this Special Issue is to bring together the works of many experts in this multidisciplinary subject that involves different competences and knowledge, which span from the study of the handwriting generation models to the development of machine learning techniques for handwriting recognition. The Special Issue should allow us to highlight the advances on these topics from a wide-angle perspective, as well as to stimulate new theoretical and applied research for better characterizing the state of the art in this subject.

The special issue should follow the 18th Conference of the International Graphonomics Society (IGS2017) that will take place from 18 to 21 June 2017 in Gaeta, Italy, but submissions will be not restricted to IGS2017 contributors.

TOPICS:
- Handwriting recognition: Human reading; Pen computing; On-line and off-line recognition;
- Cultural Heritage application: Historical document analysis and processing; Palaeography; Large digital archives.
- Forensic applications: Handwriting features; Writer identification and verification; Signature verification;
- Medical applications: Early detection and monitoring of neurological diseases implying handwriting disorders;