仕訳帳情報
Applied Soft Computing
http://www.journals.elsevier.com/applied-soft-computing/
インパクト ・ ファクター:
4.873
出版社:
Elsevier
ISSN:
1568-4946
閲覧:
11612
追跡:
22

論文募集
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
最終更新 Dou Sun 2019-12-04
Special Issues
Special Issue on Applying Machine Learning for Combating Fake News and Internet/Media Content Manipulation
提出日: 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.)
最終更新 Dou Sun 2019-11-09
Special Issue on The EAPM Young Investigator Award 2021 sponsored by Elsevier
提出日: 2020-09-30

Decisions can be made using human judgements, data analytics, or a combination of the two. With the rapid growth of data, various data analytics techniques have been adopted to explore data to find meaningful patterns to support decision making. On the other hand, a lot of decision problems are without past data, or the related data exists but is very difficult and/or expensive to obtain; in which case formulation of a suitable decision model based on ‘expert’ judgements is the main solution for decision making. Whilst many decision problems are supported with partial data or are not merely based on historical data to find patterns, hybrid techniques integrating Expert Decision Models (EDMs) into Data Analytics Algorithms (DAAs) present a promising solution for complex decision and data analytics problems. Data analytics techniques use modern statistical and machine learning mechanisms to analyze diverse kinds of data, on either a small or big scale, to discover information or knowledge for better decision making. Data analytics techniques may refer to clustering, regression, classification, association learning, reinforcement learning, evolutionary learning, deep learning, or statistical learning. EDMs are concerned with decision making techniques based on expert judgements, preferences, or opinions as inputs. EDM may refer to the research areas of multi-criteria decision making, recommender systems, user preference engineering, knowledge engineering and expert systems. This special issue aims to bring together academia and practitioners of both applied decision science and applied data science to report on the recent developments to integrate decision models based on expert judgements into data analytics algorithms to form sophisticated approaches for solving complex decision problems for various application domains. Relevant applications using Expert Decision Making for Data Analyticsinclude (but are not limited to) the following: Industrial engineering and Operational research Recommender system Social network Financial and economic applications Internet computing and Cybersecurity Bioinformatics and computational biology Medicine, health, and Wellbeing. Natural Language Processing Intelligent Transportation and Logistic Image/Video Recognition Self-organization Network Smart city Industry 4.0.
最終更新 Dou Sun 2020-08-11
Special Issue on Randomization-Based Deep and Shallow Learning Algorithms
提出日: 2020-09-30

Randomization-based learning algorithms have received considerable attention from academics, researchers, and domain workers because randomization-based neural networks can be trained by non-iterative approaches possessing closed-form solutions. Those methods are in general computationally faster than iterative solutions and less sensitive to parameter settings. Even though randomization-based non-iterative methods have attracted much attention in recent years, their deep structures have not been sufficiently developed nor benchmarked. This special session aims to bridge this gap. The first target of this special session is to present the recent advances of randomizationbased learning methods. Randomization based neural networks usually offer noniterative closed form solutions. Secondly, the focus is on promoting the concepts of noniterative optimization with respect to counterparts, such as gradient-based methods and derivative-free iterative optimization techniques. Besides the dissemination of the latest research results on randomization-based and/or non-iterative algorithms, it is also expected that this special session will cover some practical applications, present some new ideas and identify directions for future studies. Original contributions as well as comparative studies among randomization-based methods and non-randomized methods are welcome with unbiased literature review and comparative studies. Typical deep/shallow paradigms include (but not limited to) random vector functional link (RVFL), echo state networks (ESN), liquid state networks (LSN), kernel ridge regression (KRR) with randomization, extreme learning machines (ELM), random forests (RF), and so on. Topics of the special session include (with randomization-based methods), but are not limited to: Randomized convolutional neural networks Randomized internal representation learning Regression, classification and time series analysis by randomization-based methods Kernel methods such as kernel ridge regression, kernel adaptive filters, etc. with randomization Feedforward, recurrent, multilayer, deep and other structures with randomization Ensemble learning with randomization Moore-Penrose pseudo inverse, SVD and other solution procedures Gaussian process regression Randomization-based methods for large-scale problems with and without kernels Theoretical analysis of randomization-based methods Comparative studies with competing methods with or without randomization Applications of randomized methods in domains such as power systems, biomedical, finance, signal processing, big data and all other relevant areas
最終更新 Dou Sun 2020-02-23
Special Issue on New Techniques in Adversarial Machine Learning
提出日: 2020-10-20

With the rapid development of data science, machine learning has been widely applied to many important fields such as computer vision, healthcare systems, and financial predictions, to support the design of constructs of Artificial Intelligence. However, the environment of AI and machine learning is adversarial, in which machine learning tasks are faced with a variety of essential security threats coming from multiple parties. In fact, a malicious adversary can carefully manipulate input data, learning procedures, and outputs by exploiting specific vulnerabilities of learning tasks to compromise the security of machine learning systems. In terms of adversaries’ goals, the threats can be divided into three main categories: security violation, attack specificity, and error specificity. Examples include: 1) aiming to evade detection without compromising normal system operation; 2) aiming to cause misclassification of a specific set of samples, or of any sample; 3) aiming to have a sample misclassified as a specific class, or any of the classes different from the true class. To understand the security properties of Adversarial Machine Learning (AML), one should address the following main issues: 1) Identifying potential vulnerabilities of machine learning algorithms during learning, and classification; 2) Devising appropriate attacks that correspond to the identified threats and evaluating their impact on the targeted system; 3) Proposing countermeasures to improve the security of machine learning algorithms against the considered attacks. This feature topic will benefit the research community towards identifying challenges and disseminating the latest methodologies and solutions to adversarial machine learning. The ultimate objective is to publish high-quality articles presenting open issues, delivering algorithms, protocols, frameworks, and solutions. All received submissions will be sent out for peer review by at least three experts in the field and evaluated with respect to relevance to the special section, level of innovation, depth of contributions, and quality of presentation. Case studies, which address state-of-art research and state-of-practice industry experiences, are also welcome. Guest Editors will make an initial determination of the suitability and scope of all submissions. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review and the authors will be promptly notified in such cases. Submitted papers must not be under consideration by any other journal or publication Topics of interest include, but are not limited to, the following: Dependable Machine Learning Algorithms in Adversarial Setting Secure Federated Machine Learning against Malicious Attacker Security Evaluation for Federated Machine Learning Privacy Disclosure in Traditional Machine Learning Algorithms Adversarial Sample and Its Detection Method for Natural Language Processing Adversarial Sample and Its Detection Method for Images Recognition Malware Code Manipulation against Detection based on Machine Learning Verification Mechanism for Data-outsourced Machine Learning Explanation Methods for Machine Learning Application
最終更新 Dou Sun 2020-03-23
Special Issue on Intelligent solutions for efficient logistics and sustainable transportation
提出日: 2020-11-15

Over one-quarter of the global CO2 emissions are due to the transport sector, and among all means of transport, the road accounts for 74%. Solutions for reducing the emissions are vital for reaching the global climate target, set by the COP 21 Paris Agreement. However, road transportation is the backbone for most of the countries’ economy, and changes might influence on many other related sectors. It will require a lot of public effort in terms of establishing policies and providing financial assistance, passengers’ effort in terms of changing their mobility habits, and industry effort in terms of a complete update in their logistic process. Therefore, special attention is required for providing an integrated solution that leads to a sustainable and efficient road transport system whose ultimate goal is to improve passenger and trade mobility, strengthen cohesion and economy, and increase competitiveness while mitigating environmental, social and economic impacts. In this sense, any of these scenarios need to address difficult challenges which may potentially require the use of intelligent solutions and optimization strategies to achieve the expected goals. The purpose of this special issue is to collect the main recent trends targeting the design of novel innovative solutions based on cutting-edge artificial intelligence tools, optimization strategies and relevant high-impact applications in sustainable road transportation and efficient logistics. High-quality research works covering these areas are welcome including but not limited to: Improvement of logistic processes; Innovative solutions to the growing mobility using ICT capabilities; Intelligent traffic management; Minimization of transport’s impact on climate and environment; Low-emission freight transport systems and logistics; Intelligent strategies for vehicle electrification; Efficient multimodal transport network; Intelligent zero-emissions strategies and spatial planning; Driving behaviour exploration; Resources management; Process optimization; Air pollution/Smart environment; Big data, data science and visual analytics; Green transportation;
最終更新 Dou Sun 2020-03-23
Special Issue on Soft Computing for Intelligent Edge Computing
提出日: 2020-12-15

Managing Guest Editor Mohammad Mehedi Hassan (Managing Guest Editor), King Saud University, Riyadh Saudi Arabia (mmhassan@ksu.edu.sa) Guest Editors Md. Rafiul Hassan, King Fahd University of Petroleum & Minerals, Saudi Arabia (mrhassan@kfupm.edu.sa) Victor Hugo C. de Albuquerque, Universidade de Fortaleza, Brazil, (victor.albuquerque@unifor.br) Witold Pedrycz (IEEE Fellow), University of Alberta, Canada (wpedrycz@ualberta.ca) Aims and Scope Intelligent edge, i.e. the integration of edge computing with digital technologies such as artificial intelligence (AI), machine learning (ML), data analytics, big data and cloud computing, is seen as a major step towards the new revolution in the Internet-of-things (IoT) domain. Intelligent edge can be used for intelligently investigating, collecting, storing and processing the large amounts of IoT data to maximize the potential of data analytics and decision making in real time with minimum delay. In addition, intelligent edge system will not only reduce bandwidth consumption and improve response time, but can also cope with unpredictable and imprecise issues such as mobility, security, and reliability. Many of the technologies that can enable the transformation towards intelligent edge system fall within the domain of soft computing (SC), where the aim is to achieve tractability, robustness and low-cost solutions. It has been anticipated that by 2025, edge device shipments driven by artificial intelligence and SC techniques will rise from 161.4 million units to 26 billion units globally. In terms of unit quantities, the top AI and SC driven edge devices will include smartphones, PC/tablets, intelligent speakers, automotive sensors, head-mounted displays, robots for businesses and consumers, drones, and security cameras. Soft computing methodologies can use a combination of heuristics, approximation models, stochastic and non-deterministic algorithmic behavior to address various challenges in edge computing such as data accumulation, mobility, Interoperability and security. Edge-based SC can provide more prominent privacy and security in IoT network by processing the data at the source. Moreover, edge-based SC can be exceptionally flexible and adaptable. Smart devices can help to develop location-specific or industry-specific requirements ranging from medical monitoring to energy management. Furthermore, edge-based SC can offer superior experiences for customers. SC can help companies to build trust and relationship with their clients by allowing responsiveness through different services, e.g. location-aware services, or rerouting travel plans in the case of delays. Topics of Interest This special issue targets an audience of researchers, academics and industries from different communities to share and exchange new ideas, approaches, theories and practice of using Soft Computing techniques to resolve the challenging issues associated with the leveraging of intelligent edge paradigm in IoT environment. Therefore, the suggested topics of interest for this special issue include, but are not limited to: Novel network architecture and optimization method for SC applications in edge paradigm SC for efficient Big data analysis and diagnosis in edge computing Nature-inspired hybrid SC methods for intelligence edge paradigm SC for mobility, interoperability and context management in edge computing SC-based networking and communication protocols for edge computing Container based approach to implement SC in edge systems Novel deep-learning approaches for edge computing applications and services SC-enabled computation offloading in edge computing paradigm SC for trust, security and privacy management in edge system Swarm Intelligence based algorithms for edge system Benchmarking SC and ML workloads and/or frameworks on the edge Evolutionary algorithms for QoS/ QoE management in edge platform SC for autonomic resource management in edge computing Software and simulation platform for SC in edge paradigm SC for cognitive edge computing systems Important Dates Opens for submission: 1st August, 2020. Deadline for paper submission: 15th December, 2020. Notification of results: 2-3 months after submission. Final acceptance: August 30, 2021 Submission Instructions Paper submissions for the special issue should follow the submission format and guidelines for regular papers and submitted at https://ees.elsevier.com/asoc. All the papers will be peer-reviewed following Applied Soft Computing reviewing procedures. Guest editors will make an initial assessment of the suitability and scope of all submissions. Papers will be evaluated based on their originality, presentation, relevance and contributions, as well as their suitability to the special issue. Papers that either lack originality, clarity in presentation or fall outside the scope of the special issue will not be sent for review. Authors should select "VSI: SC for Intelligent Edge" when they reach the "Article Type" step in the submission process. The submitted papers must propose original research that has not been published nor currently under review in other venues.
最終更新 Dou Sun 2020-05-05
Special Issue on Recent Advances in Discrete Swarm Intelligence Algorithms for Solving Engineering Problems
提出日: 2020-12-31

The swarm intelligence-SI algorithms are generally population-based metaheuristic optimization methods. They have been developed by inspiring colonial behavior of the living creatures and applied to solve many different optimization problems in different research fields. Although there are many swarm intelligence-SI algorithms in the literature, there are still gap between swarm intelligence and engineering design problems because these SI-based algorithms have generally tested on unconstrained benchmark test sets, and their applications to engineering design problems are limited. In this virtual special issue, it is aimed to collect the research papers those are related to; Development novel swarm intelligence algorithms for engineering design problems, Novel models for the engineering problems and solution of them using swarm intelligence algorithms Development of discrete and binary solution methodology based on Swarm Intelligence for engineering design problems. Review the state-of-arts in intersection of swarm intelligence and engineering problems The scope of this special issue is to motivate and collect studies those are based on swarm intelligence, evolutionary computation and their applications to engineering problems such as optimization of design variables of an engineering problems, swarm intelligence application of discrete and binary optimization, constrained optimization etc. Submitted papers should be original, unpublished research studies and should offer fundamental research contribution either from a methodological perspective or from an application point of view. The review papers on swarm intelligence and engineering problems are also welcome to show state-of-arts. Topics of interest are challenging real-world applications of: Particle swarm optimization Ant colony optimization Artificial bee colony Grey wolf optimizer Artificial algae algorithm Tree-seed algorithm Optimization in engineering problems Constrained optimization Transportation problems Combinatorial optimization problems in engineering Real-world application of engineering problems Evolutionary computation algorithms
最終更新 Dou Sun 2020-06-25
Special Issue on Application of Computational Intelligence models in Transformative Computing technologies
提出日: 2021-01-15

Recently a Transformative Computing paradigm has been developed, and define as a new branch of modern computer sciences and information technologies. This new approach allow to join sensor signals and wireless communication technologies, with extensive signal/data analysis using AI technologies. The main idea of this emerging technology, is connection of low level signal acquisition, originating from smart or IoT sensors, with global communication, which allow to transmit and collect required data in secure manner for further semantic analysis or extensive analytical evaluation. Final data analysis should involve application of advanced soft computing or AI approaches. Such connection enhances computing possibilities, by increasing efficiency of data extraction, acquisition, and exploration, as well as performing more sophisticated analysis at different levels starting from sensor networks till augmented cognition. It can also be applicable for solving complex real life problems thanks to the application of computational intelligence approaches, oriented on using of novel methods like cognitive computing, which is based on models of human visual perception. Such human oriented data analysis and information processing approaches allow to deeply analyse a great amount of signals and information sourced from IoT, smart technologies, multimedia, and VR, manage them, and securely transmit over global networks. This Special Issue will be oriented on new possible applications of various computational intelligence methods approaches in transformative computing solutions and technologies. In particular it will be focused on complex information processing, analysis and fusion, semantic data evaluation, and secure management. Such topics, as well as many others, connected with transformative computing, and computational intelligence will form the subject of this Special Issue. Topics of interest include, but are not limited to the following application fields of soft computing methodology: Computational intelligence in transformative computing Transformative computing approaches and applications Bio-inspired models for data processing and management Cognitive approaches for information semantic analysis Transformative models for security and communication VR/AR in transformative computing Novel approaches for knowledge extraction and information analysis Human oriented protocols in transformative computing Ambient and smart technologies for data evaluation Cognitive systems in information fusion and secure management Hybrid human-AI approaches for signal/data analysis
最終更新 Dou Sun 2020-09-05
関連仕訳帳
CCF完全な名前インパクト ・ ファクター出版社ISSN
International Journal on Soft Computing AIRCC2229-7103
Applied Mathematics and Computation3.092Elsevier0096-3003
cJournal of Grid Computing1.561Springer1570-7873
Applied Computing and Informatics Elsevier2210-8327
E-Learning and Digital MediaSAGE2042-7530
Reliable Computing0.680Springer1573-1340
Journal of Big Data Springer2196-1115
cIEEE Transactions on Cloud Computing5.967IEEE2168-7161
bIEEE Transactions on Affective Computing6.288IEEE1949-3045
完全な名前インパクト ・ ファクター出版社
International Journal on Soft Computing AIRCC
Applied Mathematics and Computation3.092Elsevier
Journal of Grid Computing1.561Springer
Applied Computing and Informatics Elsevier
E-Learning and Digital MediaSAGE
Reliable Computing0.680Springer
Journal of Big Data Springer
IEEE Transactions on Cloud Computing5.967IEEE
IEEE Transactions on Affective Computing6.288IEEE
関連会議
CCFCOREQUALIS省略名完全な名前提出日通知日会議日
AICSconfArtificial Intelligence and Complex Systems Conference2020-06-152020-07-152020-08-20
NATLInternational Conference on Natural Language Computing2020-09-262020-10-222020-12-26
AECCCAfrican Electronics, Computer and Communication Conference 2020-08-202020-09-052020-09-26
SENSORNETSInternational Conference on Sensor Networks2020-10-062020-11-122021-02-09
PPSIAM Conference on Parallel Processing and Scientific Computing2017-09-11 2018-03-07
cbb1ICPADSInternational Conference on Parallel and Distributed Systems2020-07-312020-09-152020-12-02
ca1ISCASInternational Symposium on Circuits and Systems2020-10-232021-01-082021-05-23
CALCOConference on Algebra and Coalgebra in Computer Science2015-03-222015-05-062015-06-24
TDPFInternational Symposium on Technologies for Digital Photo Fulfillment2018-05-15 2018-09-24
ba*a2DCCData Compression Conference2020-11-02 2021-03-23
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