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 2019-12-04
Special Issues
Special Issue on Soft Computing for Recommender Systems and Sentiment Analysis
Submission Date: 2020-06-30

Overview The World Wide Web is becoming a bottomless source of unstructured data, with quintillions of bytes of data generated daily and publicly accessible. Social media, customer reviews and online news articles, as well as the comments associated with them, are just some examples of what the Internet is producing in terms of text data. At the same time, the World Wide Web is providing us with a great source of information about user behaviors and preferences. This has led soft computing techniques to have unmatched growth in the fields of text mining and sentiment analysis, which are adapted and applied to applications such as recommender systems. The main challenge of such systems is to filter and transform busy online information streams into structured data that can be used for decision-making. Most of the work on social media analysis has focused on textual data. The application of techniques like text mining or knowledge discovery has given birth to an important number of new different areas such as opinion retrieval, opinion summarization, subjectivity classification, among others. Furthermore, most of these textual data may also represent implicit or explicit user feedback, expressed in different formats, such as star ratings and Facebook likes, and collected from multiple sources. The combination of such multifaceted information allows for the development of new tools for recommending items according to aspects such as reputation, trust, credibility, fame, etc. Sentiment analysis techniques have provided powerful tools for decision-making in different fields, including politics, marketing, and healthcare. Some sectors also provide ad-hoc functionalities like in the case of Stocktwits, in the context of financial markets, and LinkedIn, in the context of human resources. Moreover, different soft computing techniques, such as deep neural networks and linguistic fuzzy logic, have been increasingly adopted for natural language processing and knowledge representation. Such techniques have gained increasing momentum in the past years, with a remarkable enhancement of their accuracy, and they have been sided by a boost in application-specific methodologies able to emulate the cognitive processes behind decision-making. In this special issue on soft computing for sentiment analysis and recommender systems, we aim to address the following issues: Combination of soft computing and natural language processing techniques for the development of sentiment analysis and recommender systems. Application of the recent artificial intelligence and soft computing techniques to social media mining and knowledge representation. Design and development of specific methodologies for social media analytics in the context of sentiment analysis and recommender systems. Topics of Interest Topics of interest include, but are not limited to: Community discovery and analysis in social networks. Visualization or structural analysis of social networks. Clustering and graph mining algorithms for social media. Descriptive and linguistic analysis of social media data. High-dimensional analysis of social media data. Analysis of reputation, credibility, and trust on social media data. Opinion search and meta-search. Fuzzy linguistic techniques for recommender systems. Applications of sentiment analysis and recommender systems: stock market prediction, portfolio optimization and asset allocation, trading strategies, labor market intelligence, e-commerce, e-learning, and social media marketing.
Last updated by Dou Sun in 2019-12-04
Special Issue on Applying Machine Learning for Combating Fake News and Internet/Media Content Manipulation
Submission Date: 2020-09-25

Nowadays, societies, businesses and citizens are strongly dependent on information, and information became one of the most crucial (societal and economical) values. People expect that both traditional and online media provide trustful and reliable news and content. The right to be informed is one of fundamental requirements allowing for taking right decisions in a small scale (e.g., during shopping) and large scale (e.g., during general or presidential elections). However, information is not always reliable, because digital content may be manipulated, and its spreading could be also used for disinformation. This is true especially with the proliferation of online media, where news travel fast and are often based on User Generated Content (UGC), while there is often little time and few resources for the information to be carefully cross-checked. Moreover, disinformation and media manipulation can be part of hybrid warfare and malicious propaganda. Such false content should be detected as soon as possible to avoid its negative influence on the readers and in some cases on political decisions. Part of these challenges and vivid problems can be addressed by innovative machine learning, artificial intelligence and soft computing methods. Therefore, the main aim of this special issue is to gather a set of high-quality papers presenting new approaches and solutions for media and content manipulation and disinformation detection. We also encourage papers concerning the problem of early detection of radicalization and hate speech based on fake information and/or manipulated content. The list of possible topics includes, but is not limited to: machine learning and soft computing methods for media content and disinformation analysis, especially with correlation in heterogenous types of data (images, text, tweets etc.) fake news detection in social media application of Natural Language Processing (NLP) for the disinformation analysis feature extraction algorithms for content manipulation sentiment analysis methods for fake news detection images and video manipulation recognition discovering the real content in changed images and videos early detection of radicalization/hate speech architectural frameworks and design for media content manipulation and disinformation detection blockchain applications for trusted media content learning how to detect content manipulation in the presence of the concept drift learning how to detect fake news with limited ground truth access and on the basis of limited data sets, including one-shot learning machine learning and soft computing advances in IPR and copyright challenges and protection human rights, legal and societal aspects of media content manipulation and disinformation detection case studies and real-world applications (e.g., media sector, internet content search engines, educational sector, agri-food sector, etc.)
Last updated by Dou Sun in 2019-11-09
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