Aim and Scope:
The Web has become a ubiquitous tool for finding/sharing information, and conducting business, learning and entertainment. Web intelligence (WI) strives to develop innovative solutions and frameworks that deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future web. An intelligent web is one capable of making sense in an equivalent way to how humans do. The Sentiment Web, the Sentient-Sensory Web, the Synergistic Web and the Solution-Savvy Web collectively lead to the intelligence of “Web”. Typical applications of WI such as online text classification, Web document clustering, web recommender for e-commerce, web usage profiling and similar knowledge discovery tasks are drawing attention from communities of global researchers. The knowledge-intensive and intelligent service web generates substantial amount of data that often contains complex attributes. Moreover, it is unstructured and is generated asynchronically, dynamically from web activities, for example the web usage data. To deal with the big data which is essential to web intelligence and the related applications, advanced analytics and machine learning algorithms are vital for supporting knowledge discovery.
Artificial intelligence (AI) driven models, especially deep learning models have achieved state-of-the-art results for various natural language processing, data analytics and pattern recognition tasks. Undeniably, WI presents excellent opportunities and challenges for the research and development of new generation web-based information processing technology as well as for exploiting business intelligence and AI, especially machine learning can optimize this dynamically changing and unpredictable task environment. We get highly accurate predictions using these in conjunction with large datasets, but with little understanding of the internal features and representations of the data that a model uses for information processing and data analytics. The techniques lack explanations and reasons for how and why a decision has been made. Basically, these perfect black-box machine learning techniques lack transparency and explainability. Explainable AI (XAI) is an emerging field in machine learning that aims to address how black box decisions of AI systems are made. It refers to methods and techniques in the application of AI technology such that the results of the solution can be understood by human experts. For creating human-comprehensible interpretations, it is crucial to create an equilibrium, as the common belief is that a negative relationship exists between the performance of a model and its explainability. XAI solutions will enable improved prediction accuracy with decision understanding and traceability of actions taken and have even more impact in the business/research or decision making. It aims to improve human understanding, determine the justifiability of the decision made by the machine, introduces trust and reduces bias.
The special issue aims to stimulate discussion on the design, use and evaluation of XAI models as the key knowledge-discovery drivers to recognize, interpret and process information within the socially connected ecosystem. We invite theoretical work and review articles on practical use-cases of XAI that discuss adding a layer of interpretability and trust to powerful algorithms such as neural networks, ensemble methods including random forests for delivering near real-time intelligence. Full length, original and unpublished research papers based on theoretical or experimental contributions related to understanding, visualizing and interpreting deep learning models for new generation webbased information processing technology and interpretable machine learning for exploiting business intelligence are welcome.
The list of possible topics includes, but is not limited to:
XAI for web intelligence data
Ante-hoc and post-hoc explainability techniques for AI models
Reasoning Web using XAI
Web Mining using Human-understandable AI systems
Interpretable adversarial learning for WI applications
Explainable Deep Bayesian learning for WI applications
Intelligent feature selection for interpretable web analytics
Semantic Web and XAI
Social network analysis using XAI
Web security, integrity, privacy and trust using XAI
Linguistic knowledge of deep neural networks for text mining
Natural language processing and XAI
Knowledge representation methods for online opinions
Explaining online predictions and recommendations
Interpretable multi-view representation learning for fusing disparate web data sources
Opinion spamming and intent mining using Human-understandable AI systems
Trust and interpretability in web data classification
XAI methods for Web of Things (WoT) data
Prospective authors should submit original manuscripts that have not appeared, nor are under consideration, in any other journal. Prospective authors are required to follow the Author’s Guide for manuscript submission to Information Processing & Management at: https://www.evise.com/profile/#/IPM/login. To ensure that all manuscripts are correctly identified for inclusion in the special issue, it is important that authors select “VSI: XAI-WIP” when they reach the “Article Type Selection / Issue selection” step in the submission process. Referees will consider originality, significance, technical soundness, clarity of exposition, and relevance to the special issue topics above.
Manuscript Submission Deadline: 30th December, 2020
Peer Review Due: 30th March, 2021
Revision Due: 30th May, 2021
Final Decision: 15th July, 2021
Guest Editor (s):
Dr. Deepak Kumar Jain (Managing Guest Editor)
Institute of Automation,
Chongqing University of Posts and Telecommunications, Chongqing, PR China Email: firstname.lastname@example.org, email@example.com
Google scholar: https://scholar.google.com/citations?user=QfuuEmcAAAAJ&hl=en&authuser=1
Dr. Akshi Kumar
Department of Computer Science and Engineering
Delhi Technological University, New Delhi, India
Google scholar: https://scholar.google.com/citations?user=b90f64MAAAAJ&hl=en&authuser=1
Prof. Patrick Siarry
Deputy Editor- in- chief, Engineering Applications of Artificial Intelligence
Université Paris-Est Créteil, France
Web Page: http://www.lissi.fr/p.siarry/
Google scholar: https://scholar.google.com/citations?hl=en&authuser=1&user=KI2olPkAAAAJ
Prof. Dimitrios A. Karras
National Kapodistrian University of Athens (NKUA), Greece
& Epoka University, Dep. Computer Engineering, Tirana
Email: firstname.lastname@example.org , email@example.com
Google scholar: https://scholar.google.com/citations?hl=en&authuser=1&user=IxQurTMAAAAJ
Guest Editor(s) Brief Biographies:
Deepak Kumar Jain is an Assistant Professor at Chongqing University of Posts and Telecommunications, Chongqing, China. He received the degrees of Bachelor of Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya, India and Master of Technology from Jaypee University of Engineering and Technology, India in 2010 and 2012, respectively. He completed his Ph.D. from University of Chinese Academy of Sciences, Institute of Automation,
Beijing, China. He has presented several papers in peer reviewed Conferences, as well as published numerous studies in science cited journals. His areas of research are deep learning, machine learning, pattern recognition, and computer vision.
Akshi Kumar is an Assistant Professor in the Department of Computer Science & Engineering at Delhi Technological University (formerly Delhi College of Engineering). She has received her Ph.D. in Computer Engineering from Faculty of Technology, University of Delhi, India in 2011. She has presented several papers in international conferences and published work in peer-reviewed and science cited journals. Dr. Kumar has authored a monograph ‘Web Technology: Theory and Practice’ published by CRC Press, Taylor and Francis Group and edited a book titled ‘A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development’, Springer. Dr. Kumar has guided several doctoral and post-graduate scholars. She also has two Indian Patent published. She is active reviewer of many top journals of IEEE, IET, Springer and Elsevier. In addition, she has served as a Guest Editor of IEEE and Springer journals. She is currently the guest editor for Big Data Journal, Mary Ann Liebert, Inc., publishers. — SCIE (Impact Factor: 3.446) and ACM TALLIP (Impact Factor: 1.42). Her research interests are in the area of Affective Computing, Sentiment Analysis, Big data Analytics, Artificial Intelligence and Natural Language Understanding.
Patrick Siarry (Senior Member, IEEE) was born in France in 1952. He received the Ph.D. degree from the University Paris 6, in 1986, and the D.Sc. (Habilitation) degree from University Paris 11, in 1994. He was first involved in the development of analog and digital models of nuclear power plants at Electricité de France (E.D.F.). Since 1995, he has been a Professor in automatics and informatics. His main research interests are computer-aided design of electronic circuits, and the applications of new stochastic global optimization heuristics to various engineering fields. He is also interested in the fitting of process models to experimental data, the learning of fuzzy rule bases, and of neural networks.
Dimitrios A. Karras received his Diploma and M.Sc. Degree in Electrical and Electronic Engineering from the National Technical University of Athens, Greece in 1985 and the Ph. Degree in Electrical Engineering, from the National Technical University of Athens, Greece in 1995, with honours. From 1990 and up to 2004 he collaborated as visiting professor and researcher with several universities and research institutes in Greece. Since 2004, after his election, he has been with the Sterea Hellas Institute of Technology, Automation Dept., Greece as associate professor in Digital Systems and Signal Processing as well as with the Hellenic Open University, Dept. Informatics as a visiting professor in Communication Systems (the latter since 2002 and up to 2010). His research interests span the fields of pattern recognition and neural networks, image and signal processing, image and signal systems, biomedical systems, communications, networking and security. He has served as program committee member in many international conferences, as well as program chair and general chair in several international workshops and conferences in the fields of signal, image, communication and automation systems. He is, also, editor in chief of the International Journal in Signal and Imaging Systems Engineering (IJSISE), academic editor in the TWSJ, ISRN Communications and the Applied Mathematics Hindawi journals as well as associate editor in various scientific journals. He has been cited in more than 1400 research papers.