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Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, U.K.
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2 Papers

Scientific Data Analysis using Neo4j

Topics: Information Systems; Information Management ; Emerging Products, Services and Research; Technologies for Finance, Economics, Management And/Or IT Business; Informatics and Digital Transformation; ICT for Business, Education, Healthcare, Finance and Other Sectors

Authors: Victor Chang 1 , Yeshwanth Kumar Songala 2 , Qianwen Ariel Xu 2 and Ben Shaw-Ching Liu 3

Affiliations: 1 Department of Operations and Information Management, Aston Business School, Aston University, Birmingham, U.K. , 2 Cybersecurity, Information Systems and AI Research Group, School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, U.K. , 3 Department of Marketing, Lender School of Business Center, Quinnipiac University, Hamden, CT 06518 U.S.A.

Digital Object Identifier: 10.5220/0011036700003206

Proceedings of FEMIB 2022, ISBN: 978-989-758-567-8

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There is new material regarding Neo4j algorithm questions posed in this report as well as where we did our due diligence research to resolve them. These results often provide details, including inquiries recorded after each monitoring period. Initially, a literature review supported the development of an initial data modeling, which was further provided as experimental analysis. By using Neo4j Graph Database, graph analytics can provide useful analysis.

Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase

Topics: Internet of Things; Networking and Communication Protocols ; Systems for IoT and Services Computing

Authors: Han Xu 1 ; 2 , Zheming Zuo 3 , Jie Li 4 and Victor Chang 4

Affiliations: 1 Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China , 2 School of Microelectronics, University of Chinese Academy of Sciences, Beijing 100049, China , 3 Department of Computer Science, Durham University, Durham DH1 3LE, U.K. , 4 Cybersecurity, Information Systems and AI Research Group, School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS3 6DR, U.K.

Digital Object Identifier: 10.5220/0011102800003194

Proceedings of IoTBDS 2022, ISBN: 978-989-758-564-7

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Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However, unseen class label prediction is far less explored due to missing classes being invisible in training ML or DL models. In this work, we propose a fuzzy inference system to cope with such a challenge by adopting TSK+ fuzzy inference engine in conjunction with the Curvature-based Feature Selection (CFS) method. The practical feasibility of our system has been evaluated by predicting the positioning labels of networking devices within the realm of the Internet of Things (IoT). Competitive prediction performance confirms the efficiency and efficacy of our system, especially when a large number of continuous class labels are unseen during the model training stage.

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