Bipartite Split-Merge Evolutionary Clustering

Boeva, Veselka; Angelova, Milena; Devagiri, Vishnu Manasa; Tsiporkova, Elena

doi:10.1007/978-3-030-37494-5_11

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…This can be achieved by developing unsupervised and semisupervised methods to automate knowledge extraction and learning in data stream scenarios [4], [5]. The main problem investigated is how the newly arrived information can be taken into account in the learning phase and can be used for continuous adaptation of the learned model [6]. In addition, it will be studied how to develop, train, and evaluate a model with no direct access to labeled data.…”

Section: Research Objectivesmentioning

confidence: 99%

“…1) dynamic unsupervised and semi-supervised learning models that are robust to the appearance of drifting context and additionally enable to learn from multiple data sources by distributed training, and continual updating and evolving of the model [4], [6], [7], [8]; 2) development of dynamic techniques for automatic annotation (labeling) of the data; 3) usage of transfer learning techniques enabling reuse of knowledge from training in earlier tasks to subsequent tasks. The other research ambition of DAISeN is the design of distributed/composable data mining models.…”

Section: Research Objectivesmentioning

confidence: 99%

“…According to [58], different phases of an evolutionary clustering algorithm can be categorized into matching, accommodating new data, and model refinement. Dynamic clustering is also a form of online/incremental unsupervised learning [4], [6], [7], [9], [60]. However, it considers the incremental fashion of building the clustering model and self-adaptation of the built model.…”

Section: B Current State Of the Art And Challengesmentioning

confidence: 99%

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Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN)

Boeva¹,

Casalicchio²,

Abghari³

et al. 2022

Annals of Computer Science and Information Systems

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This position paper describes the aims and preliminary results of the Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN) 1 project. The project ambition is to address today's edge AI challenges by developing advanced AI techniques that model knowledge from the sensor network and the environment to support the deployment of sustainable AI applications. We present one of the use cases being considered in DAISeN and review the state-of-the-art in three research domains related to the use case presented and directly falling into the project scope. We additionally outline the main challenges identified in each domain. The developed Global Navigation Satellite Systems (GNSS) activation model addressing the use case challenges is also briefly introduced. The future research studies planned for the remaining period of the project are finally outlined. I. INTRODUCTIONT HE NUMBER of solutions that provide Artificial Intelligence (AI) and Machine Learning (ML) based systems has been growing recently. These solutions facilitate the creation of new smart products and services in many different fields. In addition, sensor networks are undergoing great expansion and development and the integration of AI and sensor networks benefits many areas such as Industry 4.0, healthcare, mobility, logistics, and many other Internetof-Things (IoT) applications. However, this has also put new challenges in front of researchers and practitioners. New realtime AI and ML algorithms are needed along with different strategies to embed these algorithms in sensor boards and network nodes such as fog/edge nodes. For example, edgebased AI requires robust and adaptive models that take into account the temporal component of a data flow and allow for vertical and horizontal scaling of the decision-making process. These models must employ efficient learning algorithms that are capable of dealing with information varying over time and coping with large scale missing and inaccurate values. In addition, the decision-making models should be composable so that they can be distributed on the edge devices in order to This work is part of the Sony RAP 2020 Project "Distributed and Adaptive Edge-based AI Models for Sensor Networks"1 Daisen is a volcanic mountain located in Tottori Prefecture, Sanin Region of Japan. ensure a trade-off between the decision accuracy, latency, and consumed energy per decision.The IoT is an emerging key technology for future industries and the everyday lives of people, e.g., it has been playing an increasingly important role in healthcare, agriculture, home services, industrial processes, and transportation. Wireless Sensor Network (WSN) is an enabling technology for IoT [1], and, by definition, is the bridge between the physical world and the intelligence residing on the Internet. The integration of AI and sensor networks (by means of the IoT) are now realities that are changing our lives. Sensor networks are widely used to collect environmental parameters, e.g., in homes, buildings, and vehicles, where they a...

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Section: Research Objectivesmentioning

confidence: 99%

Section: Research Objectivesmentioning

confidence: 99%

Section: B Current State Of the Art And Challengesmentioning

confidence: 99%

See 1 more Smart Citation

Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN)

Boeva¹,

Casalicchio²,

Abghari³

et al. 2022

Annals of Computer Science and Information Systems

Self Cite

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show abstract

“…For example, in 2019, Boeva et al focus on, the selection of a personalised treatment plan for a patient by using a split-merge evolutionary clustering algorithm, that can be used for maintaining patient profiles in healthcare [76]. In using clustering techniques, it was discovered that in associating patientspecific disease characteristics the person's increased risk for cardiovascular disease can be identified [77].…”

Section: Addressing Current Gapsmentioning

confidence: 99%

An IoT Approach to Personalised Remote Monitoring and Management of Epilepsy

Mchale

Pereira

Weishmann

et al. 2017

2017 14th International Symposium on Pervasive Systems, Algorithms and Networks &Amp; 2017 11th International Conference on Fro

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I am grateful to the individuals that participated in the clinical studies described in this thesis. I would like to thank them for their patience, generosity, and courage. Thanks to Dr. Udo Weishmann his mentorship and input while I carried out this project; our initial fortuitous meeting was to make-way for this influential collaboration. Thanks, Udo, for sharing the passion to help epilepsy patients which has motivated us throughout. Thanks to Becky Higginbottom and Amy Ibbotson, and all the Clinical Physiologists' at the Walton Centre for assisting me with patient profile collection and teaching me valuable epilepsy terminology and the methods at the Neurophysiology Department.Thanks to the NRES committee London-Dulwich (HEALTH RESEARCH AUTHORITY) for supporting me in the design of clinical studies, and for helping me with the NHS IRAS (Integrated Research Approval System). Thanks to Dave Watling for making the ethical aspects of the studies at The Walton Centre possible. The App system used in these studies could not have been built without the help of my colleague and fellow PhD researcher Oladotun Omosebi: his determination, positivity and skill will long live in the memory. I am hugely thankful for the input from my supervisory team; Professor

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