Journal Information
Applied Soft Computing
http://www.journals.elsevier.com/applied-soft-computing/
Impact Factor:
3.907
Publisher:
ELSEVIER
ISSN:
1568-4946
Viewed:
8436
Tracked:
16

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Call For Papers
Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems. Soft computing is a collection of methodologies, which aim to exploit tolerance for imprecision, uncertainty and partial truth to achieve tractability, robustness and low solution cost. The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities.

Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.

Major Topics:

The scope of this journal covers the following soft computing and related techniques, interactions between several soft computing techniques, and their industrial applications:

• Fuzzy Computing
• Neuro Computing
• Evolutionary Computing
• Probabilistic Computing
• Immunological Computing
• Hybrid Methods
• Rough Sets
• Chaos Theory
• Particle Swarm
• Ant Colony
• Wavelet
• Morphic Computing

The application areas of interest include but are not limited to:

• Decision Support
• Process and System Control
• System Identification and Modelling
• Engineerin Design Optimisation
• Signal or Image Processing
• Vision or Pattern Recognition
• Condition Monitoring
• Fault Diagnosis
• Systems Integration
• Internet Tools
• Human-Machine Interface
• Time Series Prediction
• Robotics
• Motion Control and Power Electronics
• Biomedical Engineering
• Virtual Reality
• Reactive Distributed AI
• Telecommunications
• Consumer Electronics
• Industrial Electronics
• Manufacturing Systems
• Power and Energy
• Data Mining
• Data Visualisation
• Intelligent Information Retrieval
• Bio-inspired Systems
• Autonomous Reasoning
• Intelligent Agents
• Multi-objective Optimisation
• Process Optimisation
• Agricultural Machinery and Produce
• Nano and Micro-systems
Last updated by Dou Sun in 2018-08-11
Special Issues
Special Issue on Bio-Inspired Optimization Techniques for BioMedical Data Analysis: Methods and Applications
Submission Date: 2019-11-30

The intertwining disciplines of bio-inspired computing (BIC), biomedical imaging and data analysis are major fields of computer science, computer engineering and electrical and electronic engineering, which have attracted the interest of many researchers. The past and on-going research covers a wide range of topics and tasks, from fundamental research to a huge number of real-world industrial applications. An exhaustive search is impractical in solving problems. Optimization provides a powerful tool for solving learning problems and data analysis. Designing and implementing optimization algorithms are based on several methods and have superior performance in many problems. However, in several applications, the search space increases exponentially with the problem size. In order to overcome the limitations and to solve efficiently larger scale of combinatorial and highly nonlinear optimization problems, sets of more flexible and adaptable algorithms are compulsory. BioMedical data analyses are driving new optimization research trends mainly based on machine learning and artificial intelligence, motivating intersections with biomedical imaging & data analysis and systems development. Bio-inspired computing is oriented toward applying outstanding information-processing aptitudes of the natural realm to the computation domain. It establishes a strong relationship with computational biology and other biology-inspired computing models due to its effectiveness and uniqueness even though it is still relatively new trend. Some meta-heuristic search algorithms with population-based framework are capable of handling optimization in high-dimensional real-world problems in several domains including engineering, medicine, industry, education, and military. The discipline of Bio-inspired optimization algorithms is a major field of computational intelligence, soft computing and optimization at large, which has attracted the interest of many researchers. These algorithms provide efficient tools to those problems, which cannot be solved using traditional and classical mathematical methods, as often the algorithms do not require any mathematical condition to be satisfied. The overall aim of this special issue is to collect state-of-the-art contributions on the latest research and development, up-to-date issues, and challenges in the fields of Bio Inspired Computing and BioMedical Data Analysis, and related applications. Proposed submissions should be original, unpublished, and present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view. The topics of interest are strictly limited to: 1. New theories and methods in different BIC paradigms applied to Biomedical data analysis, such as - Ant Colony Systems - Artificial Immune Systems - Artificial Neural Networks - Cellular Automata - Cognitive Modelling - DNA Computing - Differential Evolution - Emergent Systems - Evolutionary Computations - Evolutionary Strategies/Programming - Genetic Algorithms/Programming - Granular Computing - Organic Computing - Particle Swarm Optimization - Swarm-based Algorithms 2. Applications of BIC and BIC-related techniques to biomedical data analysis, including - Biomedical intelligent decision support system - Computer aided diagnosis - Parallel processing - Biomedical applications - Internet of Health Things - Health 4.0 - Virtual environments and Bio-inspired robotics - Automatic feature extraction and construction in complex images - Medical and bio-medical data analysis - eHealth, mHealth and Telemedicine
Last updated by Dou Sun in 2018-07-07
Special Issue on Soft Computing for Network and System Security of Internet of Everything
Submission Date: 2019-12-31

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
Last updated by Dou Sun in 2019-04-12
Special Issue on Immune Computation: Algorithms & Applications
Submission Date: 2020-01-15

I. AIM AND SCOPE Immune Computation, also known as "Artificial Immune System", is a fast developing research area in the computational intelligence community, inspired by the information processing mechanism of biological immune system. Many of these algorithms are built on solid theoretical foundations, through understanding mathematical models and computational simulation of aspects of the immune system. The scope of this research area ranges from modeling to simulation of the immune system, to the development of novel engineering solutions to complex problems, and bridges several disciplines to provide new insights into immunology, computer science, mathematics and engineering. This special issue is an activity of the IEEE CIS Task Force on Artificial Immunes Systems. The aims of this special issue are: (1) to present the state-of-the-art research on Artificial Immune Systems, especially the immune-based algorithms for real-world applications; (2) to provide a forum for experts to disseminate their recent advances and views on future perspectives in the field. II. THEMES Following the development of AISs, the topics of this special issue will focus on the novel immune algorithms and their real-world applications. Topics of interest include, but are not limited to: 1. Immune algorithms Clonal selection algorithms Immune network algorithms Dendritic cell algorithms Negative/positive selection algorithms Negative representations of information Hybrid immune algorithms Novel immune algorithms 2. Applications Immune algorithms for optimization, including multi-objective optimization, dynamic and noisy optimization, multimodal optimization, constrained optimization, large scale optimization Immune algorithms for security, including intrusion detection, anomaly detection, fraud detection, authentication Immune-based privacy protection schemes as well as sensitive data collection Immune-based data mining techniques Immune algorithms for pattern recognition Immune algorithms for robotics and control Immune algorithms for fault diagnosis Immune algorithms for big data Immune algorithms bioinformatics
Last updated by Dou Sun in 2019-06-09
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