计算机类 | SCI期刊专刊信息6条

全文截稿: 2019-01-10

影响因子: 2.218

CCF分类: 无

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 工程：综合 - 3区

• 小类 : 仪器仪表 - 3区

网址: https://www.journals.elsevier.com/measurement

One of the basic problems in science is the ability to measure the environment, with the goal of supporting a hypothesis. The management of uncertainty in decision making problems is a very challenging research issue, because deterministic or probabilistic classical decision approaches quite often do not fit well to real world decision making problems. In spite of the existence of many tools to model and manage such an uncertainty depending on the uncertain situation, some tools may be more suitable than others. Recently, it was introduced the concept of neutrosophic sets. Neutrosophic sets and logic are generalizations of fuzzy and intuitionistic fuzzy sets and logic.

Neutrosophic sets and logic are gaining significant attention in solving many real-life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. A number of new netrosophic theories have been proposed and have been applied in Multi-criteria decision making, computational intelligence, image processing, medical diagnosis, fault diagnosis, optimization design, and so on.

The use of information fusion techniques in decision making processes have been also widely provided in the literature. Information fusion is a process that associates, correlates, and combines information from multiple sources to obtain a relevant and timely view of the situation and it is the premise for predictions about development and significance of the situation aiming for decision making. The advances in information fusion imply improvements or new methods in decision making.

The aims of this Special Issue are: (1) to present the state-of-the-art research on neutrosophic theory and its application in information fusion to manage uncertainty in decision making, and (2) to provide a forum for researchers to discuss the latest progress, new research methodologies, and potential research topics.

Topics of Interest:

We welcome authors to present new techniques, methodologies, mixed method approaches and research directions unsolved issues. Topics of interest include, but are not limited to:

- Neutrosophic set-based multimodal sensor data

- Neutrosophic set-based array processing and analysis

- Wireless sensor networks

- Neutrosophic set-based Crowd-sourcing

- Neutrosophic set-based heterogeneous data mining

- Internet of things (IoT)

- Web data fusion and mining

- Scalable and multimedia big data fusion

- Uncertainty modeling in heterogeneous data

- Measure and capacity integrals

- Interpretable and transparent fusion algorithms

- Definition and generation of comprehensive models for heterogeneous fused data

- Neutrosophic outranking methods

- Multisensor images

- Advances in Decision Making Under Uncertainty, Ignorance, Inconsistency, and Vagueness in information fusion

- Intelligent decision making in information fusion

- Neutrosophic logic-based fusion algorithms

- Indeterminate models in information fusion

全文截稿: 2019-01-15

影响因子: 3.444

CCF分类: B类

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 计算机：信息系统 - 3区

网址: http://www.journals.elsevier.com/information-processing-and-management/

Information Need is one the most significant and controversial concepts within Information Science. Information needs are fundamental to much research within Information Seeking and Retrieval and we have seen decades of research into how to classify information needs, how systems should respond to different types of information need and into the nature of information needs themselves. Information need has also proven to be a very accessible concept, one that appears widely across disciplines and appears extensively within both the research and practitioner literatures.

In spite of its strong place within many sub-fields of Information Science, Information Need is also a highly contentious concept. Many authors feel that it is a concept that lacks theoretical clarity or that other concepts, such as tasks or situations, better represent what users care about. Although information ‘need’ has an intuitive and common-sense feeling about it, this property may well mask serious problems with one of our most important theoretical constructs.

The Special Issue is intended to present a collection of significant contributions to our understanding of the concept of Information Need. Contributions to this issue will go beyond simply describing information needs in order to answer critical questions regarding the nature of information needs. Submissions may answer theoretical questions on how we conceptualise need or perform comparisons between information need and alternative concepts. They may empirically demonstrate new knowledge about information needs in new contexts or provide new ways to distinguish between information needs. They may also help us evaluate how to support information needs of different types or present new methodologies for understanding information needs.

This Special Issue is inspired by Robert Taylor’s 1968 classification of four levels of information need [1]. One of the most cited and influential works within Information Science, Taylor’s research was a major theoretical advance. Half a century later we wish to both honour this significant intellectual contribution and reflect on the status of Information Need within Information Science.

Submissions should fall under at least one of these categories, if in doubt as to suitability, please contact the Guest Editors:

- Definition and conceptualisation of Information Need

- Empirical investigations of Information Need

- Methodological innovation in studying Information Need

- Information Need in novel contexts

全文截稿: 2019-01-30

影响因子: 1.196

CCF分类: 无

中科院JCR分区:

• 大类 : 工程技术 - 4区

• 小类 : 计算机：硬件 - 4区

• 小类 : 计算机：信息系统 - 4区

网址: https://www.journals.elsevier.com/sustainable-computing

Scope: In the current era of information and communication technology, it has been critical to develop sustainable energy-efficient networking technologies for the meeting the growing demand of ICT applications such as health care, smart cities, business, and entertainment. In designing sustainable computing technologies, communication networks and sustainable devices have to meet the quality of service requirements of ICT applications efficiently. Networking technologies and devices play a critical role in communication networks. Thus, there are a number of research challenges in reducing energy consumption in systems and in developing energy efficient wireless communication networks, resource management, green cloud computing strategies, and router and server architecture design.

This special issue seeks original and unpublished research articles reporting all aspects including theoretical studies, practical applications and experimental prototypes pertaining to sustainable networking technologies.

Specific topics include, but not limited to, the following:

Sustainable mobile networks

Smart sensors and sustainable computing

Next generation networking infrastructure and sustainability

Energy-efficient cloud computing technologies

IoT and sustainable computing for Smart Healthcare and Smart cities

Energy-efficient wireless mobile communications

Security, trust, and privacy in sustainable mobile computing and communications

Cyber-physical systems for sustainable computing

Efficient data management for sustainable mobile computing and communications

Big data processing for energy-efficient mobile computing

Energy-efficient networking for smart environments.

全文截稿: 2019-01-31

影响因子: 2.423

CCF分类: 无

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 自动化与控制系统 - 3区

• 小类 : 计算机：人工智能 - 3区

• 小类 : 工程：电子与电气 - 3区

• 小类 : 工程：机械 - 2区

网址: https://www.journals.elsevier.com/mechatronics

Automatic transmissions play extremely important roles in improving fuel economy, drivability and performance. Due to the increasing customer demands and more stringent regulations, more and more attention has been paid to transmission modeling, analysis and control to effectively and efficiently overcome those challenges. Recently, a lot of progress has been made in terms of control and actuator approaches to realize high robustness and accuracy, which is leading to higher comfort level and efficiency for automatic transmissions.

Main intention of this special issue is to provide a premier international platform for wide range of professions including scholars, researchers and engineers to share the most cutting-edge progress in transmission control and actuators technologies involved and their relationship to analytic procedures. This special issue will also discusses procedures for the validation, identification, analysis of vehicle transmissions and its best practices for the real world. Manuscripts making fundamental or practical contributions on Modeling, Analysis and Control of Vehicle Transmissions are solicited, including, but not limited to, the following topics:

1) System modeling, identification and optimization for automatic transmissions
2) System design of novel automatic transmissions
3) Vibration analysis of automatic transmissions
4) Synthesis and generation of novel automatic transmissions
5) Robust control and filtering for automatic transmissions
6) Shift control of automatic transmissions
7) Clutch-to-clutch control of automatic transmissions
8) Signal processing of automatic transmission
9)Soft computing methods in instrumentation and signal processing of automatic transmission
10) Fault diagnosis, prognosis, and healthy monitoring system design
11) Advanced industrial applications on various vehicles

全文截稿: 2019-03-01

影响因子: 1.196

CCF分类: 无

中科院JCR分区:

• 大类 : 工程技术 - 4区

• 小类 : 计算机：硬件 - 4区

• 小类 : 计算机：信息系统 - 4区

网址: https://www.journals.elsevier.com/sustainable-computing

In the present era machine learning (ML) based technologies have been revolutionizing and reshaping the global world with high sustainability to get them up to mark healthcare platform. Besides, sustainable autonomous and self-driven methods are getting closer attention from every corner to supplement the medical world with the modern trends and practices. Sustainable computing-based techniques are the key role players in most of the areas including industry, healthcare, and games among others. These applications need high visibility, productivity and innovative technologies for the betterment of each sector. Furthermore, the emerging role of the sustainable computing applications has dignified the importance and role of the various sectors especially, medical healthcare which is the cornerstone of today’s aging society. The computer-assisted living is one of the examples in the medical world to diagnose and examine the critical features of the common and elderly citizens effectively. On the one hand this domain has caught the attention, and on the other hand, there is a lack of proper interaction between humans to computers and computer to sustainable computer systems with strong and retainable capabilities. Thus, to obtain the longer and green environment self-adaptive and effective monitoring methods are the cornerstones of today’s aspiring need. Emerging sustainable computing enabled applications with the involvement of machine learning methods not only have facilitated every corner of the world but also opened the doors with various directions to promote every desired landscape. Machine learning is the prominent and inspiring ingredient with high strength in numerous areas for example, sustainable industrial and home automation, image processing, efficient and sustainable diagnosis in medical healthcare, etc. Due to a broader scope, it has been integrated with every moment of human needs where sustainable computing based emerging technologies and trends are playing the major role in developing the entire world.

The key purpose of this special issue is to integrate the academic and industrial thoughts by adopting the machine learning based sustainable and emerging computing applications to promote every sector for the betterment of the society.

Mainstreams are given below with a broader scope but not limited to, the following:

· Sustainable Computing based applications in healthcare domain
- Power-aware and battery-efficient sustainable communication systems
- Wireless power transfer based sustainable systems
- Sustainable Bluetooth low energy and LoRa

· Machine learning and deep learning for sustainable computing bio-informatic systems
- Q-learning based sustainable cloud-computing platform for pervasive healthcare
- Sustainable architectures and algorithms for Telemonitoring
- Sustainable and adaptive Internet of medical things

· Neural Network and Re-enforcement based sustainable frameworks/architectures and algorithms for the medical internet of things
- Fuzzy-based sustainable and QoS-aware body sensor networks
- Sustainable and Energy Harvesting based medical applications
- 5G-aware sustainable and battery friendly approaches
- Green and sustainable biomedical systems

· Self-adaptive and resource-aware sustainable healthcare technologies
- Secure and sustainable mobile healthcare platform
- QoS/QoE management and monitoring in the sustainable mobile healthcare

· Blockchain-based frameworks and strategies for sustainable Ambient living
- System-level optimization, cross-layer coordination
- Sustainable computing based decentralized systems for healthcare
- Secure and sustainable architectures and approaches for elderly healthcare

· Big data analytics in sustainable computing-based technologies.
- Sustainable computing-based data mining algorithms and frameworks

· Graphical summarization and visualization techniques for sustainable system
- Sustainable human computer interaction platform

全文截稿: 2019-03-10

影响因子: 2.218

CCF分类: 无

中科院JCR分区:

• 大类 : 工程技术 - 3区

• 小类 : 工程：综合 - 3区

• 小类 : 仪器仪表 - 3区

网址: https://www.journals.elsevier.com/measurement

The measurement techniques in medical imaging, especially in ultrasonic, magnetic resonance (MR) and computer tomography (CT) imaging has been proved to be valuable. Texture features of an image to be classified are often used as inputs to a computer-aided diagnosis (CAD) system for discriminating between normal and abnormal tissues or diseases. However, in many medical applications, the search space rises exponentially with the problem size. This would increase the complexity of solving real world problems in disease diagnosis. Especially, large volume of high-dimensional medical image data, including MRI, CT, X-Ray, Ultrasound, Elastography, Photoacoustic, radiographic images have been generated by advanced medical devices. These high-dimensional multimedia data bring new challenges and opportunities to researchers as well. In order to overcome this issue, many researchers have developed numerous measurements techniques and algorithms. The objective of this special issue is to collect state-of-the-art contributions on the recent trends and development, issues, and challenges in the fields of measurement techniques for medical image processing. Topics of interest include, but are not limited to:

The following is a non-exhaustive list of topics considered for this special issue:

- Measurement instruments for 2D and 3D medical images

- Image editing and restoration techniques in medical image measurements

- Image Enhancement methods in medical image measurements

- Displacementmeasurement techniquesin medical imaging

- DigitalImageCorrelation in 2D and 3D medical image measurements

- Performance analysis of various medical image measurements

- High brightness beam measurement techniques in medical imaging

- Image based measurements techniques in disease diagnosis

- Innovative measurement techniques in medical imaging

- Estimation techniques in medical image measurements

- Advanced data processing for medical imaging

- Fusion algorithms for medical image measurements

- Evaluation procedures for medical image measurements

- Quantitative angle measurement in medical imaging

- Mathematical models for medical image measurements

- Distributed measurement systems in medical imaging

- Sampling errors in medical image measurements

- Novel segmentation methods in medical image measurements

- Novel Feature extraction methods in medical image measurements

- Multi-scale signal analysis in medical image measurements

- Feature projection and pattern recognition in medical image measurements

- Innovative measurement techniques for MRI, CT, X-Ray, Ultrasound, Elastography, Photoacoustic, radiographic images

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