Journal Information
Applied Soft Computing
Impact Factor:

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 2017-08-05
Special Issues
Special Issue on Advanced Soft Computing for Prognostic Health Management
Submission Date: 2017-10-01

Prognostic health management (PHM), studying machine failure detection and management of its life-cycle, is a research area of growing interest because of the economic cost associated with undiagnosed machine failure. A complex manufacturing plant usually consists of a number of massive inter-related components. A failure of a particular component frequently imposes a complete shutdown of the plant process meaning a complete stop of the production cycle. An equipment failure imposes millions of dollars in costs for repair materials, labour and interruption of production cycles, since components are manufactured half way across the world and there may only be few places where they are manufactured. Aging of machinery and its components makes machinery vulnerable to failures. This problem cannot be completely addressed by regular maintenance, carried out at pre-scheduled time periods and requires "maintenance on-demand", during the specific time period, when the machine is likely to fail. The optimization of machinery service and the minimization of life-cycle costs demand advanced soft computing approaches to predict when a machine will no longer be able to perform with satisfactory functionality as well as to monitor a machine condition while running the process without interruption and to alert operators when a fault comes into picture. Prognostics or prediction of the remaining useful life (RUL) plays a crucial role in PHM to provide accurate decision support for maintenance on-demand. While fault detection has been well researched, the prognostics of the likely occurrence of a fault before it occurs has recently started to be a major focus of investigation. Note that accurate prediction of a machinery's RUL leads to flexibility of maintenance on demand such as advanced scheduling of maintenance activities, proactive allocation of replacement parts and enhanced fleet deployment decisions based on the estimated progression of component life consumption. The prediction of RUL aims to make use of the monitoring information of in-service machinery and its past operation profile in order for RUL to be identified before a failure occurs. Nonetheless, development of a reliable predictive methodology to feed accurate information of lifetime of machinery or to monitor tool condition in real-time remains a very complex issue to be dealt with. This special issue aims to bring together research works of soft computing including but not limited to metaheuristic, fuzzy system, neural system, hybrid and probabilistic systems with application to the PHM. Special attention will be paid toward algorithmic development of advanced soft computing to address advanced issues of PHM in various application domains. The main topics of this special session include, but are not limited to, the following: [Basic Methodologies] - Advanced soft computing for fault detection and diagnosis - Advanced soft computing for tool condition monitoring - Advanced soft computing for estimation of tool's remaining useful life [Advanced Concepts] - Appropriate handling of data uncertainty in various forms in PHM - Data stream analytics for PHM - Big data analytics for PHM - Techniques to address drifts and shifts for PHM - On-line dynamic dimension reduction for PHM - Feature selection and extraction techniques for PHM - Sample selection and active learning for PHM - Reliability in model predictions and parameters for PHM - Domain adaptation, importance weighting and sampling for PHM - Parameter-low and -insensitive learning methods for PHM - On-line complexity reduction to emphasize transparent, more compact models for PHM - Unsupervised approach for PHM - Anomaly detection for PHM - Outlier detection for PHM - Noise Cancellation for PHM [Applications] - Complex manufacturing process - Data stream modelling and identification (supervised and unsupervised) - Online fault detection and decision support systems - Online media stream classification - Predictive maintenance and prognostics - Fault isolation - Process control and condition monitoring - Modelling in high throughput production systems - Adaptive chemometric models in dynamic chemical processes - High-speed machining process - Robotics, Intelligent Transport and Advanced Manufacturing - Optimization of complex manufacturing systems - Feedback control systems - Intelligent Control Systems
Last updated by Dou Sun in 2017-05-12
Special Issue on Applying Machine Learning Systems for IoT Services in Industrial Informatics
Submission Date: 2017-11-20

Machine learning techniques are delivering a promising solution to the industry for building Internet of Things (IoT) systems and to make innovation at a rapid pace. The Open IoT cloud platform offers a framework for building large scale IoT applications relying on data gathered from a complex infrastructure of sensors and smart devices. Numerous challenges exist in implementing such a framework, one of them being to meet the IoT data and services (quality of service (QoS)) requirements on Industrial informatics based applications in terms of energy efficiency, sensing data quality, network resource consumption, and latency. The new era of convergence of machine learning techniques (supervised-unsupervised and reinforcement learning) with reference to IoT quality of data and services for Industrial applications has three main components: (a) intelligent devices, (b) intelligent system of systems, and (c) end-to-end analytics. This special issue is integrating machine learning methods, advanced data analytics optimization opportunities to bring more computer IoT data and services. Further, machine learning approaches had addressed various challenges of IoT such as anomaly detection, multivariate analysis, streaming and visualization of data. In fact, recent literatures have addressed the inherent power of fusion between machine learning algorithms and IoT applications in industrial informatics. It can provide effective solutions for machine understanding of data (structured/semi structured), optimization problems, specifically, dealing with incomplete or inconsistent information, with limited computational capability related to Internet of Things (IoT). This special issue aims to address the machine learning techniques, recent developments in diverse IoT data, services and applications as well as theoretical studies. Besides, we can consider that machine learning re-enforcement paradigms and predictive learning algorithms are more applicable to IoT datasets, time series data from IoT devices with sensor fusion and streaming. Further, it is important to make a note that machine learning systems and optimization techniques has not been adequately investigated from the perspective of IoT data and services (Quality of Services) and its related research issues in industrial applications. Furthermore, there are many noteworthy QoS metrics (system life time, latency, quality, delay, bandwidth and throughput) that need to be addressed in the view of machine learning algorithms with relate to IoT data and services. Obviously, these challenges also create immense opportunities for researchers. For the aforementioned reasons, this special issue focuses to address comprehensive nature of machine learning and to emphasize its character in modelling, identification, optimization, prediction, forecasting, and control of future IoT systems for industrial systems. Submissions should be original, unpublished, and present in-depth fundamental research contributions either from a methodological/application perspective in understanding machine learning approaches and their capabilities in solving diverse range of problems in IoT and its real-world industrial applications. We seek original and high quality submissions related to (but not limited to) one or more of the following topics: (Note that this special issue emphasizes "real world" applications) - Design and Evaluation of Energy Efficient Networks and Services in IoT - Machine-Learning and Artificial Intelligence for Traffic/Quality of Experience Management in IoT - Hybrid Intelligent Models and Applications for IoT in Industrial applications - Nature-Inspired Smart Hybrid Systems for IoT Context-Aware Systems - Machine learning and Data Analytics and Decision Automation in IoT for Industry - Knowledge-Based Discovery with Evolutionary Algorithms for QoS in IoT devices - Fuzzy Fusion of Sensors, Data and Information - Meta-Heuristic Algorithms for IoT and wearable Computing - Hybrid Optimization Methods Emerging real world and theoretical applications of IoT in Industry - Innovative Deep Learning Architectures/Algorithms for Time Series Data and IoT - Neural network modelling, analysis and synthesis techniques in ubiquitous communications - Multi-Objective IoT System Modelling and Analysis—Performance, Energy, Reliability, Robustness - Modelling and simulation of large-scale IoT scenarios and IoT standardization - Machine learning for IoT and sensor research challenges: battery of sensor, routing, prediction of nodes etc. - Quality aspects in the IoT (e.g., runtime dependability, assurances, validation, verification, privacy, security) - State-of-practice, experience reports, industrial experiments, and case studies in the IoT
Last updated by Dou Sun in 2017-08-05
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