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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, advance and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Swarm Intelligence 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: Evolutionary Computing Fuzzy Computing Hybrid Methods Immunological Computing Neuro Computing Swarm Intelligence Machine and Deep Learning Rough Sets The application areas of interest include but are not limited to applications of soft computing to: Agricultural Machinery, Smart Farming Autonomous Reasoning Big Data, IoT, Edge Computing Combinatorial Optimization Data Mining Decision Support Engineering Design Optimization Fault Diagnosis Finance Human-Machine Interface Intelligent Agents Manufacturing Systems Power Electronics Multi-objective Optimization Power and Energy Process and System Control Robotics Security Sensor Systems Signal or Image Processing Software Engineering Supply Chain Economy System Identification and Modelling Telecommunications Time Series Prediction Extended Reality, Metaverse, Digital Twins Vision or Pattern Recognition Authors are welcome to submit letters promoting original soft computing research to Applied Soft Computing's open access companion title, Systems and Soft Computing.</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-07-12</div> </div> </div> <div class="portlet" id="yw2"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-list-alt"></i> Special Issues</div> </div> <div class="portlet-content"> <a name="5485"></a><pre>Special Issue on Industrial Large Models</br>Submission Date: 2025-04-30</br></br>This special issue explores the transformative potential and challenges of Industrial Large Models (ILMs). Guest editors: Prof. Enrique E. Herrera-Viedma University of Granada, Granada, 18071, Spain E-mail: viedma@decsai.ugr.es Prof. Jiehan Zhou Shandong University of Science and Technology, Qingdao, 266590, China E-mail: jiehan.zhou@ieee.org Prof. Yuhai Liu Zhengzhou University, Zhengzhou, 450001, China E-mail: yuhailiu@qq.com Prof. Chunsheng Yang Guangzhou University, Guangzhou, Guangdong, 510006, China E-mail: chunsheng.yang@carleton.ca Prof. Wei WeiHuazhong University of Science and Technology, Wuhan, Hubei, 430074, China E-mail: weiw@hust.edu.cn Special issue information: Special issue introduction: We define Industrial Large Models (ILMs) as a transformer-based model used in industrial applications, especially those with a vast number of parameters capable of processing and generating multimodal data (such as text, images, and sound). These models, through large-scale data pre-training, possess strong generalization capabilities and can handle multiple tasks without explicit guidance. Industrial large models suppose to be used in automated design, production process optimization, product quality control, and more. The emergence of ILMs marks a new era in the application of artificial intelligence in industry. With the development of Large Language Models (LLMs), industrial application has entered a new phase of development. ILMs promise to process and understand massive amounts of multi-modal data, support a variety of complex tasks, driving automation and enhancing industrial intelligence in the product research and development, design, simulation, production, testing, operation, and after-sales phases. This results in strong generalization capabilities across multiple industrial tasks such as automated design, production process optimization, and quality control.This special issue aims to gather insights from academics, researchers, and professionals working at the intersection of Deep Learning and LLMs, and industrial applications, contributing to the advancement and understanding of Large Language Models in industrial settings. Full scope of the Special Issue: The emergence of Industrial Large Models (ILMs) marks a new era in the application of artificial intelligence in industry. With the development of Large Language Models (LLMs), industrial intelligence has entered a new phase of development. ILMs promise to process and understand massive amounts of multi-modal data, support a variety of complex tasks, driving automation and enhancing intelligence in industries. This results in strong generalization capabilities across multiple industrial tasks such as automated design, production process optimization, and quality control. This special issue aims to gather insights from academics, researchers, and professionals working at the intersection of Deep Learning and LLMs, and industrial applications, contributing to the advancement and understanding of Large Language Models in industrial settings. Manuscript submission information: Important Dates: Submission deadline: April 30, 2025Final Decision: July 31, 2025</pre><div class="alert alert-warning">Last updated by <a href="/researcher/1">Dou Sun</a> in 2025-03-09</div><a name="5361"></a><pre>Special Issue on Intelligent Decision Making, Deep and Machine Learning, Generative AI to Empower Smart Production and Service</br>Submission Date: 2025-05-31</br></br>Deep and machine learning, swarm intelligence, big data analytics, generative artificial intelligence, and LLM (Large Language Model) are increasingly developed for intelligent decision making involved in smart production and service in various industrial contexts. To meet with various needs of different customers, paradigm is shifting from mass production and mass-customization towards personalization and flexibility for smart production and service. In addition to the challenges in real settings, more opportunities are made available by state-of-the-art soft computing and AI technologies to empower novel applications for smart production and service to enhance supply chain resilience and change the business ecosystem. Guest editors: Dr. Chen-Fu Chien, National Tsing Hua University, Taiwan; Dr. Kanchana Sethanan, Department of Industrial Engineering, Khon Kaen University, Thailand; Dr. Run-Liang Dou, Department of Information Management and Management Science, Tianjin University, China; Dr. Rapeepan Pitakaso, Department of Industrial Engineering, Ubon Ratchathani University, Thailand; Dr. Chia-Yu Hsu, Department of Industrial Management, National Taiwan University of Science and Technology, Taiwan Manuscript submission information: Flexible decisions and intelligent decision making involved in production and service processes, manufacturing networks, supply chain management, and industry ecosystem are critical to enhance the efficiency and effectiveness of human-system collaborations to empower smart production and service. This special issue of the Applied Soft Computing aims to address research issues involved in shifting paradigms driven by the developments of intelligent decision making, deep and machine learning, swarm intelligence, big data analytics, generative artificial intelligence, and LLM for smart production and service in various industrial contexts. Empirical studies with technical and/or methodological advances to address realistic issues are encouraged. In particular, high-tech industries such as semiconductor industries consist of complex and lengthy manufacturing processes with tightly constrained processing technologies, reentrant process flows, sophisticated equipment, volatile demands, and high product mix. While big data is accumulated via the fully automated production and service facilities and supply chain management systems, various solutions and techniques can be developed to empower intelligent decision making to address new challenges in real time. Scope of this Virtual Special Issue: Topics to be covered include the application of the following artificial intelligence and soft computing methodologies and interactions between several soft computing techniques: Ant Colony Optimization Artificial Intelligence Big Data Analytics Convolutional Neural Networks Deep and Machine Learning Evolutionary Computing Fuzzy Computing Generative AI Hybrid Methods Immunological Computing Intelligent Decision-Making Technologies LLM (Large Language Model) Neuro Computing Particle Swarm Optimization Probabilistic Computing Rough Sets Wavelet to address critical, not restricted to the following aspects of smart production in real settings: Advanced equipment/process control (AEC/APC) Automated material handling systems (AMHS), automatic guided vehicle (AGV) Intelligent Decision technologies for Real-time Decision Equipment diagnosis, Predictive maintenance, and Tool Health Factory modeling, analysis and performance evaluation Flexible Production Planning & Scheduling Industry 4.0 & Manufacturing Strategy Intelligent systems & Robots Manufacturing Intelligence & Manufacturing Informatics Mass personalization and customization Predictive Maintenance Smart Decision for Corporate Resource Planning & Allocation Sustainability and circular economy Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR) Yield enhancement and e-Diagnosis Important date: Deadline for Submission: 31 May, 2025 Final Decision: 31 August, 2025</pre><div class="alert alert-warning">Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-10-27</div><a name="5476"></a><pre>Special Issue on Soft Computing for Modern Engineering: Addressing Environmental Challenges</br>Submission Date: 2025-07-30</br></br>In the face of pressing environmental concerns and the need for responsible resource management, achieving sustainable engineering practices has become a central focus across various industries. Traditional engineering approaches often struggle with the inherent complexities of these challenges, which frequently involve multi-criteria decision-making and the need to balance environmental, economic, and social objectives. This special issue, titled "Soft Computing for Modern Engineering: Addressing Environmental Challenges", proposes to explore the growing potential of soft computing techniques as powerful tools to bridge this gap. Soft computing, encompassing methodologies like fuzzy logic, neural networks, and evolutionary algorithms, offers a unique ability to handle uncertainty and ambiguity - characteristics that are prevalent in real-world sustainability problems. Guest editors: Dr. Masoomeh Mirrashid Abu Dhabi University, UAEmirrashid.research@adu.ac.ae Prof. Abdollah Shafieezadeh Ohio State University, USAshafieezadeh.1@osu.edu Prof. Hosein Naderpour Semnan University, Irannaderpour@semnan.ac.ir Prof. Mahdi Kioumarsi Oslo Metropolitan University, Norwaymahdik@oslomet.no Special issue information: This special issue will welcome original research articles and reviews that showcase the innovative application of soft computing methodologies in sustainable engineering. Potential topics include, but are not limited to: Energy Systems Optimization: Leveraging fuzzy logic and neural networks for optimizing energy consumption in buildings and renewable energy integration. Sustainable Materials Development: Utilizing evolutionary algorithms and fuzzy sets for designing eco-friendly materials and optimizing manufacturing processes. Life Cycle Assessment (LCA): Employing machine learning techniques to analyze the environmental impact of products and systems throughout their life cycle. Risk Management and Decision Support: Developing hybrid fuzzy-neural models for assessing environmental risks and supporting sustainable decision-making in engineering projects. Smart Infrastructure Systems: Implementing soft computing techniques for intelligent control and optimization of sustainable infrastructure systems like transportation networks and water management. Multi-Objective Optimization: Exploring how soft computing can handle multiple, often conflicting, objectives in sustainable engineering problems. This could involve optimizing cost, environmental impact, and performance simultaneously. Uncertainty Modeling and Management: Highlighting the application of soft computing for dealing with inherent uncertainties and complexities in sustainability challenges. This could involve fuzzy logic for representing imprecise data or probabilistic models for risk assessment. Data-Driven Sustainability Analysis: Showcasing the use of soft computing techniques for analyzing large datasets related to sustainability. This could include machine learning for identifying patterns in energy consumption data or using natural language processing to analyze sustainability reports. Soft Computing for Policy and Regulation Development: Exploring how soft computing can inform the development of sustainable policies and regulations. This might involve using evolutionary algorithms to design incentive programs for sustainable practices or employing fuzzy logic to assess environmental compliance. Soft Computing for Education and Awareness: Investigating the role of soft computing in promoting sustainability education and raising awareness. This could involve developing interactive learning platforms or using natural language generation to create engaging sustainability content. Social Sustainability: Encouraging submissions that explore the intersection of soft computing and social aspects of sustainability, such as promoting equitable access to resources or fostering community engagement in sustainable development projects. Life Cycle Thinking: Highlighting the use of soft computing for assessing the environmental, social, and economic impacts of products and systems throughout their entire life cycle. This could involve combining LCA methodologies with soft computing techniques. Sustainable Urban Development: Welcoming research on how soft computing can contribute to creating sustainable and resilient cities. This could encompass traffic management systems, resource optimization in buildings, or smart grid applications. Circular Economy: Exploring the use of soft computing for optimizing resource use and promoting circular economy principles in engineering design and manufacturing. This might involve designing for disassembly and recyclability or optimizing waste-to-resource conversion processes. Climate Change Mitigation and Adaptation: Showcasing how soft computing can contribute to addressing climate change challenges. This could involve developing models for climate change impact assessment, designing strategies for mitigation and adaptation, and optimizing resource allocation for climate action initiatives. Sustainable Concrete: Utilizing soft computing techniques to optimize the design, performance, and sustainability of concrete as a building material. This could include employing genetic algorithms and fuzzy logic to design low-carbon concrete mixes, using neural networks to predict the durability and lifespan of concrete structures under varying environmental conditions, or applying machine learning methods to enhance the recycling and reuse of concrete materials.</pre><div class="alert alert-warning">Last updated by <a href="/researcher/1">Dou Sun</a> in 2025-02-08</div><a name="5486"></a><pre>Special Issue on Robust and Secure AI Systems</br>Submission Date: 2025-07-31</br></br>The rapid development of AI has ushered in transformative possibilities across various sectors, such as transportation, robotics, and healthcare. However, this proliferation of AI technologies has brought complex safety and reliability challenges. Ensuring the robust deployment of AI systems and safeguarding them against adversarial attacks and unintended consequences are paramount concerns in the realm of AI research and application. Guest editors: Dr. Peilan Xu Nanjing University of Information Science and Technology, School of Artificial Intelligence Nanjing, 210044, China E-mail: xpl@nuist.edu.cn Prof. Wenjian Luo Harbin Institute of Technology Shenzhen, School of Computer Science and Technology HIT Campus, Xili University Town, Shenzhen, 518055, China E-mail: luowenjian@hit.edu.cn Dr. Hansheng Lei The University of Texas Rio Grande Valley, Department of Informatics and System Engineering West University Blvd, SETB 1.506, UTRGV, Brownsville, TX 78521 E-mail: hansheng.lei@utrgv.edu Special issue information: Aim and Scope: The rapid development of AI has ushered in transformative possibilities across various sectors, such as transportation, robotics, and healthcare. However, this proliferation of AI technologies has brought complex safety and reliability challenges. Ensuring the robust deployment of AI systems and safeguarding them against adversarial attacks and unintended consequences are paramount concerns in the realm of AI research and application. Recently, significant strides have been made in advancing the frontiers of robust and secure intelligence. State-of-the-art attack techniques and defense methods play a vital role in improving the security of AI systems and protecting user data and privacy. Moreover, the concept of robust intelligence has emerged as a critical area of focus, which enables AI systems to perform well under uncertain environment, ensuring their stability and reliability against unforeseen challenges. These advances have enabled breakthroughs in areas, mitigating the risks associated with AI systems solving real world problems. Despite the remarkable progress, challenges persist. Ensuring that AI models are safe, robust, interpretable, and unbiased remains a pressing concern. Additionally, the vulnerability of AI systems to adversarial attacks and uncertain environment poses a significant threat, requiring robust and secure innovative solutions to fortify these systems against malicious manipulation. Addressing these challenges is central to creating trustworthy and secure AI systems that can be reliably deployed in real-world environments. The aims of this special issue are: (1) to bring together cutting-edge research that addresses the robustness and security challenges in AI, with a specific focus on robust and security intelligence. (2) to provide a platform for researchers and practitioners to propose innovative, practical, and implementable solutions and present their views on future research trends in building robust and secure intelligence systems. Themes: Following the development of artificial intelligence, the topics of this special issue will focus on the new robust and secure intelligence techniques and their applications. Topics of interest include, but are not limited to: Robust optimization and decision Robust AI architectures Adversarial attack and defense techniques on AI models Data and privacy-preserving techniques for AI models Secure training and deployment in distributed AI models Explainability and interpretability in robust AI Human-AI collaboration and trust Real-world applications of robust and secure intelligence Manuscript submission information: Important Dates: Submission Deadline: July 31, 2025 Final Date (Accept/Reject): November 30, 2025</pre><div class="alert alert-warning">Last updated by <a href="/researcher/1">Dou Sun</a> in 2025-03-09</div></div> </div>  <div class="portlet" id="yw3"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-file"></i> Related Journals</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw4" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> 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Name</th><th id="yw5_c1">Impact Factor</th><th id="yw5_c2">Publisher</th></tr> </thead> <tbody> <tr class="odd"> <td><a href="/journal/746">Integrated Computer-Aided Engineering</a></td><td>5.800</td><td>IOS Press</td></tr> <tr class="even"> <td><a href="/journal/1108">Intelligence & Robotics</a></td><td></td><td>OAE Publishing</td></tr> <tr class="odd"> <td><a href="/journal/109">Data & Knowledge Engineering</a></td><td>2.700</td><td>Elsevier</td></tr> <tr class="even"> <td><a href="/journal/104">ACM Transactions on Information Systems</a></td><td>5.400</td><td>ACM</td></tr> <tr class="odd"> <td><a href="/journal/832">Journal of Digital Imaging</a></td><td>2.900</td><td>Springer</td></tr> <tr class="even"> <td><a href="/journal/362">Computers and Electronics in Agriculture</a></td><td>7.700</td><td>Elsevier</td></tr> <tr class="odd"> <td><a href="/journal/1068">Entropy</a></td><td>2.100</td><td>MDPI</td></tr> <tr class="even"> <td><a href="/journal/773">IEEE Transactions on Haptics</a></td><td>2.400</td><td>IEEE</td></tr> <tr class="odd"> <td><a href="/journal/438">Advances in Data Analysis and Classification</a></td><td>1.400</td><td>Springer</td></tr> </tbody> </table><div class="keys" style="display:none" title="/journal/343"><span>746</span><span>1108</span><span>109</span><span>104</span><span>832</span><span>362</span><span>1068</span><span>773</span><span>438</span></div> </div></div> </div> </div> <div class="portlet" id="yw6"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-th"></i> Related Conferences</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw7" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw7_c0">CCF</th><th id="yw7_c1">CORE</th><th id="yw7_c2">QUALIS</th><th id="yw7_c3">Short</th><th id="yw7_c4">Full Name</th><th id="yw7_c5">Submission</th><th id="yw7_c6">Notification</th><th 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href="/conference/1411">SIAM Conference on Parallel Processing for Scientific Computing</a></td><td>2023-06-30</td><td> </td><td>2024-03-05</td></tr> <tr class="even"> <td><span class="badge badge-warning">c</span></td><td></td><td><span class="badge badge-warning">a1</span></td><td>ISCAS</td><td><a href="/conference/340">International Symposium on Circuits and Systems</a></td><td>2024-10-14</td><td>2025-01-20</td><td>2025-05-25</td></tr> <tr class="odd"> <td><span class="badge badge-warning">c</span></td><td><span class="badge badge-warning">b</span></td><td><span class="badge badge-warning">b1</span></td><td>ICPADS</td><td><a href="/conference/223">International Conference on Parallel and Distributed Systems</a></td><td>2024-07-07</td><td>2024-08-15</td><td>2024-10-10</td></tr> <tr class="even"> <td></td><td></td><td></td><td>CDICS</td><td><a href="/conference/4563">International Conference on Data, Information and Computing 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href="/conference/340">International Symposium on Circuits and Systems</a></td><td>2024-10-14</td><td>2025-05-25</td></tr> <tr class="odd"> <td>ICPADS</td><td><a href="/conference/223">International Conference on Parallel and Distributed Systems</a></td><td>2024-07-07</td><td>2024-10-10</td></tr> <tr class="even"> <td>CDICS</td><td><a href="/conference/4563">International Conference on Data, Information and Computing Science</a></td><td>2024-11-05</td><td>2024-12-06</td></tr> <tr class="odd"> <td>MLAMDA</td><td><a href="/conference/4673">Asia Conference on Machine Learning, Algorithms, Modeling and Data Analysis</a></td><td>2023-09-23</td><td>2023-12-08</td></tr> <tr class="even"> <td>CALCO</td><td><a href="/conference/1711">Conference on Algebra and Coalgebra in Computer Science</a></td><td>2015-03-22</td><td>2015-06-24</td></tr> </tbody> </table><div class="keys" style="display:none" title="/journal/343"><span>3678</span><span>2715</span><span>3753</span><span>1770</span><span>1411</span><span>340</span><span>223</span><span>4563</span><span>4673</span><span>1711</span></div> </div></div> </div> </div> <div class="portlet" id="yw9"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-eye-open"></i> Recommendation</div> </div> <div class="portlet-content"> <div align="center"> <script async src="//pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"></script>  <ins class="adsbygoogle" style="display:block" data-ad-client="ca-pub-6743617430731451" data-ad-slot="1870704128" data-ad-format="autorelaxed"></ins> <script> (adsbygoogle = window.adsbygoogle || []).push({}); </script> </div> </div> </div> </div> <div class="span2 hidden-phone"> <div class="sidebar-nav"> <ul id="yw10"> <li><a href="http://www.myhuiban.com/journal/track/id/343"><i class="icon icon-plus"></i>   Track It <span class="badge badge-warning 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