Impact Score 5.45
Aims, Scope and Objective:
The application of data science techniques in the smart ecosystems has surged since the beginning of this decade as this offer potentially new and meaningful information about the considered system. The recent growth of data because of advancements in Internet of things (IoT) and other related technologies has made it more imperative to leverage the data science techniques for understanding and interacting with the local environment (humans/devices/systems). The data generated in various smart ecosystems ranging from transportation to healthcare constitutes too big data where the traditional analytical techniques would not be much effective. To cater to the large set of data in such ecosystems, deep learning techniques can help to find various interesting patterns in data and help solve various underlying problems. These deep learning-based analytical schemes combined with the other traditional schemes such as neural networks, fuzzy logic reasoning, and other machine learning schemes can even help to automate the big data processing in such smart ecosystems. Moreover, the emergence of Artificial Intelligence (AI) as an enabler for the success of smart ecosystems (comprising smart cities, smart grid, intelligent transportations, healthcare, industrial systems and many more) has provided a new direction for the modern research community. The embedded intelligence in smart devices has made it necessary to articulate and utilize learning/self-learning models for optimizing or enhancing the performance vectors in smart ecosystems. However, the voluminous and varied data streams enforce a tough challenge for the sustainability of performance for real-time or near to real-time applications. The emergence of Industry 4.0 and Heath 4.0 make it further important to collect/sense every bit of data, analyze it locally or remotely, and extract intelligence for providing improved decision making. This brings a new revolution in the terms of the Internet of Systems, wherein different systems interact among each other through a web of connected machines for costeffective operations and enhanced performance. A large amount of data (volume), which is frequently transformed (velocity) and generated in different formats (variety), should be computationally analyzed for the identification of the hidden patterns and associations (internal/external) to articulate the potential benefits to the larger application or the entire systems thereof. Keeping this in focus, the focus of this topical collection proposal will be on the deep neuro-fuzzy based analytics for intelligent analysis and processing of big data in smart ecosystems. The researchers, both at the academic and industrial level, working on emerging problems in these domains can share their novel solutions and latest results. The topical collection seeks original and unpublished research on the topics related to deep neuro-fuzzy analytics for intelligent big data processing in smart ecosystems. The suitable topics include, but are not limited to, the following:
Dr. Gagangeet Aujla (lead guest editor), Newcastle University, UK,
Dr. Anish Jindal, University of Essex, UK, [email protected]
Dr. Danda Rawat, Howard University, USA, [email protected]
Dr. Chunxiao Jiang, Tsinghua University, China, [email protected]
Paper submissions for the special issue should follow the submission format and guidelines (https://www.springer.com/journal/521/submission-guidelines).
All the papers will be peer-reviewed following the NCA Journal reviewing procedures. Authors should select ‘SI: Deep Neuro-Fuzzy Analytics in Smart Ecosystems’ during the submission step 'Additional Information'.
The submitted papers must provide original research that has not been published nor currently under review by other venues. Previously published conference papers should be clearly identified by the authors at the submission stage and an explanation should be provided about how such papers have been extended to be considered for this special issue.
Guest editors will make an initial determination 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 and the authors will be promptly informed in such cases.
A Peer Review procedure will follow in order to perform an objective and robust review of all the manuscripts. Every manuscript will be sent to at least 3 international reviewers, with recognized experience in the field.