Journal Information
Applied Soft Computing
http://www.journals.elsevier.com/applied-soft-computing/
Impact Factor:
5.472
Publisher:
Elsevier
ISSN:
1568-4946
Viewed:
13754
Tracked:
25
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 2021-03-07
Special Issues
Special Issue on Predictive Intelligence: Humans Meet Artificial Intelligence
Submission Date: 2021-05-31

Artificial Intelligence (AI) has seized the attention of the business world. AI is the next step on the journey from big data to full automation. Human needs are the motivation behind improvements in computing paradigms. Examples of this include such things as collecting brainwave data via “wearables” and using that information to monitor health and predict issues, tracking the movements of mobile phones on roads to predict traffic jams (Google Maps), and using natural language processing to learn and “predict” correct spelling and offer human-like speech (Amazon Alexa, Apple Siri). The more data that is collected, the wider the variety of predictions that can be offered. Each of these examples indicates an implicit or explicit need or expectation from humans, and each is an attempt to satisfy that need via a specific approach. However, humans expect more as technology develops. To this end, AI continuously interacts with us by simulating our thinking patterns, behaviors, and bringing other relevant information into play. Given the number of similar studies in this field, we suggest the introduction of a new computing paradigm, “Predictive Intelligence.” “Predictive Intelligence” utilizes three types of data: (1) training data for building the AI model, (2) input data for prediction functionality, and (3) feedback data for tuning the model parameters and improving the prediction accuracy. Strong predictions also serve as inputs that are factored into subsequent decisions. The economic field has developed a reliable framework that aids in the understanding of how decisions are made. Recent advances in prediction technology have created implications that are not well-understood, and decision theory derived from economics can provide deeper insight. “Predictive Intelligence” outperforms humans when the complex interactions of various dimensions are considered, especially when huge amounts of data are involved. Increasing the number of interacting dimensions exposes the progressive limitation of the human ability to make accurate predictions, especially when compared to the abilities of a machine. On the other hand, humans often outperform machines—especially when small amounts of data are involved—because their ability to understand the process that generates the data gives them a prediction advantage. This phenomenon offers the opportunity to raise challenging issues within the field of computer science. Machine learning refers to the design and analysis of algorithms with which computers can "learn" automatically, allowing machines to generate rules by analyzing data and employing that data to “predict” unknowns. Machine learning has been applied to solve complex problems in human society for years, and it has been successful because of advances in computing capabilities and sensing technology. As artificial intelligence and soft computing approaches evolve, they will soon have a considerable impact on the field. Recently, deep learning has matured in the field of supervised learning. Machine learning is only incipient in such areas as unsupervised learning and reinforcement learning using methodologies that involve soft computing. Developments in artificial intelligence and high-speed computing performance have brought recent dramatic changes. Thus, deep learning serves as an excellent example of using feature engineering to exceed the limits of machine learning, offering far greater performance and making possible a number of extremely complex applications. Prediction can be broken into four distinct categories: known knowns, known unknowns, unknown unknowns, and unknown knowns. “Predictive Intelligence” deals extremely well with known knowns. Machine learning works best with rich data. “Predictive Intelligence” is good at filling in the gaps around known unknowns. These are things humans know intuitively, but machines cannot know. The “unknown” is used in the sense of the discovery of something not known previously. Data can be hard to collect because the rarity of some events makes them a challenge to predict. Unlike machines, humans excel at making predictions with small amounts of data. The majority of “deep learning” technologies build on the concept of supervised learning to determine classifiers that allow the system to recognize various data patterns or events. A Generative Adversarial Networks (GAN) can also overcome the “too little data” issue that creates the known unknowns bottleneck. In order to generate a prediction, human experts and knowledge engineers need to inform the machine regarding the kinds of things for which a prediction is valuable. Unprecedented events cannot be predicted by a machine as they have never occurred. Therefore, the machine is disoriented by data of which it is unaware or that is entirely unexpected, i.e., the unknown unknowns. The Google Flu Trends (GFT) was a failed attempt at using machine intelligence to predict unknown unknowns. This means that linking predictions and effective predictions can also incur some risks and bias. “Soft computing and metaheuristic algorithms” are applicable to this situation. The concept of unsupervised learning is then used to determine efficacious solutions within a solution space, the infinite space in which the unknown unknowns issues can be overcome. Finally, the greatest weakness of “Predictive Intelligence” is the unknown knowns, i.e., when the wrong answer is provided with complete confidence that it is actually right. That sends AI down the wrong path. If the decision process by which the data was generated is not fully understood by the machine, prediction failure is likely. Therefore, large scale incremental learning and transfer learning methods can be used to detect possible knowns from the current knowns and ameliorate this weakness. For this special issue, we solicit original contributions that address challenges and issues relating to the exploitation of soft computing or deep learning methods to build prediction models and resolve situations involving unknown unknowns and unknown knowns. Classification names should not be derived by learning from past knowns but rather from predicting the expected answer. The best predictions are achieved when humans and machines work in combination, as the strengths of each makes up for the weaknesses of the other. The main goal of this special issue is to collect manuscripts reporting the latest advances in standards, models, algorithms, technologies and applications, and to highlight the paradigm shifts in this field. We solicit original contributions that fall within, and each submission must contribute to soft computing related methodology, the following topics of interest: - Methodologies, and Techniques Adaptive machine learning and soft computing algorithms for data streams New methods combining soft computing and deep learning New learning methods involving soft computing concepts for extant architectures and structures of predictive intelligence Evolutionary and soft computing-based tuning and optimization of predictive intelligence Metaheuristics aspects and soft computing algorithms in deep learning for improved convergence of predictive intelligence Robust data augmentation methods for predictive intelligence learning Faster incremental learning and transfer learning methods for predictive intelligence self-learning - Human Behavior Human behavior and user interfaces for human-centered predictive intelligence Human participation and social sensing for human-centered predictive intelligence The applications of personality and social psychology for predictive intelligence Artificial intelligence and mental processes in human-centered predictive intelligence Trust, security, and privacy issues for human-centered predictive intelligence - Real-World Applications Economic and financial applications Intelligent e-learning & tutoring Internet of Things (IoT) applications Smart healthcare Social computing Smart living and smart cities
Last updated by Dou Sun in 2020-11-03
Special Issue on Fuzzy Systems for Biomedical Science in Healthcare
Submission Date: 2021-11-30

Scope of the issue The world issues to deal with the pandemic caused by the pathogen SARS-CoV-2 has urgently posed the need of rethinking the available resources to combat a health crisis of this dimensions. Innovation in healthcare needs to be accelerated to address the health problems of our time and the future. Biomedical and healthcare data are available in different formats, including numeric, textual reports, images, and the data may come from different sources. A major challenge in biomedical science and healthcare involves coping with the uncertainty, imprecision and incompleteness. Such uncertainties make it difficult to develop useful models, algorithms, systems, and realizing their successful applications. Although the current research in this field has shown promising results, there is an urgent need to explore novel data-driven knowledge discovery and analytics methods in clinical research to improve epidemic monitoring and healthcare delivery as a whole. Intelligent medicine and healthcare decision support systems have become an emerging research topic since they can be applied for disease diagnostics and/or prevention, follow-up monitoring, defining treatment pathways, clinical decision support etc. Despite the significant recent advances in medicine and healthcare data analysis, there are substantial research challenges and open questions to be explored. These demand further and deeper investigations to develop more useful decision-making systems that are capable of dealing with randomness, imprecision, volume, vagueness, incompleteness, and missing values along with efficient handling of variety, velocity and (abundant or lacking) volume of biomedical data. Compared to the traditional decision support techniques, the representation of fuzzy linguistic terms based on soft computing provides a straightforward framework for building more understandable, imprecision-aware clinical systems. As opposed to systems powered by statistical reasoning only, fuzzy biomedical systems cater a way of building models that encode the imprecise conceptual semantics of a health problem, not just for doing analytics, but also to embrace its interpretability. Thus, designing an efficient and effective fuzzy system to deal with uncertainty is an emerging and promising topic to improve reasoning and intelligent monitoring, control, diagnostic and treatment in biomedical science in healthcare. Topics In this special issue we will consider submissions in that deal with innovative research works that face contemporary research issues of biomedical engineering in healthcare. Articles that are not innovative enough in their clinical research questions and applications will not be considered for publication in this special issue. The topics include but are not limited to: Fuzzy systems for predicting and monitoring the spread of epidemic diseases Fuzzy systems for measuring the damage of the epidemic disease IT2 fuzzy sets for uncertain healthcare datasets Fuzzy approaches for neuroimaging and functional brain imaging processing of COVID-19 Fuzzy learning models for feature extraction of COVID-19 Fuzzy medicine and healthcare data mining based on the Hadoop or Spark platforms Fuzzy system for patient planning and health services Multi-objective evolutionary and adaptive fuzzy systems for handling epidemic disease Fuzzy models for medical image classification/ diagnosis /recognition Fuzzy data mining for brain-machine interfaces and medical signal analysis Fuzzy classification for multi-modality image fusion for analysis, diagnosis, and intervention Real-world applications of fuzzy system for future challenges of COVID-19
Last updated by Dou Sun in 2021-02-11
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