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Marina Ninoslav

Marina Ninoslav

Maître d'enseignement HES

Main skills

Blockchain et Smart Contract

Cryptography

Signal Processing (Images/Video)

Cybersecurity

Machine Learning

Wireless Communication

Statistics

  • Contact

  • Teaching

  • Publications

Main contract

Maître d'enseignement HES

Haute Ecole Arc - Ingénierie
Espace de l'Europe 11, 2000 Neuchâtel, CH
DING
BSC HES-SO en Informatique et systèmes de communication - Haute Ecole Arc - Ingénierie
  • Python et introduction aux données
BSc HES-SO en Informatique - Haute Ecole Arc - Ingénierie
  • Traitement numérique du signal
BSc HES-SO en Industrial Design Engineering - Haute Ecole Arc - Ingénierie
  • Python

2022

Tools for Analytics and Cognition for Crowd Journalism Application
Scientific paper

Marina Ninoslav

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Springer International Publishing, 2022 , vol.  443, pp.  252-263

Link to the publication

Summary:

Businesses and service consumers should take advantage of social media's ability to adapt their marketing campaigns to achieve a long-term strategic advantage. Setting quantitative and attainable expectations is critical to the progress of every marketing or business endeavour. The development of tools for analytics and cognition (TAC) is essential for customers and providers to increase productivity and inject intelligent insights into operational and mission-critical social media businesses through driven analytics. In this paper, the developed tools provide guided analytics software for intelligent aggregation, cognition and interactive visualization with a monitoring dashboard for concrete crowd journalism use cases. The provider receives an approach to a guided analytic dashboard filled with meaningful business visualization predictions. Among the other things, he can inspect the quantitative metrics for a sharing economy and estimate stakeholders' channel monetization as a new innovative quantified value by engaging users with trusted content. TAC uses this principle of engagement rate measurements and provides visualization insights for stakeholders to choose the right track for boosting their business.

A Hybrid Machine Learning Approach of Fuzzy-rough-k-nearest Neighbor, Latent Semantic Analysis, and Ranker Search for Efficient Disease Diagnosis
Scientific paper

Marina Ninoslav, Sunil Kumar Jha, Jinwei Wang, Zulfiqar Ahmad

Journal of Intelligent and Fuzzy Systems, 2022 , vol.  42, no  3, pp.  2549-2563

Link to the publication

Summary:

Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12%of the primary tumor, 96.45%of breast cancer Wisconsin, 94.44%of cryotherapy, 93.81%of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively.

Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering
Scientific paper

Marina Ninoslav, Nikita Karandikar, Rockey Abhishek, Nishant Saurabh, Zhiming Zhao, Alexander Lercher, Radu Prodan, Chunming Rong, Antorweep Chakravorty

Blockchain: Research and Applications, 2022 , vol.  2, no  2, pp.  100-016

Link to the publication

Summary:

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand. Flattening the usage curve can result in cost savings, both for the power companies and the end users. Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks. In addition, demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks. In this work, we present a data driven approach for incentive-based peak mitigation. Understanding user energy profiles is an essential step in this process. We begin by analysing a popular energy research dataset published by the Ausgrid corporation. Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns. We implement, and performance test a blockchain-based prosumer incentivization system. The smart contract logic is based on our analysis of the Ausgrid dataset. Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.

Optimizing Anonymity and Performance in a Mix Network
Scientific paper

Marina Ninoslav, Mathieu Jee, Ania Piotrowska, Harry Halpin

Foundations and Practice of Security. FPS 2021. Lecture Notes in Computer Science, 2022 , vol.  13291, pp.  53-62

Link to the publication

Summary:

Mix networks were developed to hide the correspondence between senders and recipients of the communication. In order to be usable and defend user privacy, anonymous communication networks like mixnets need to be parameterized in an optimal manner. This work uses a mixnet simulator to determine reasonable packet size and parameters for the real-world Nym mixnet, a stratified continuous-time mixnet that uses the Sphinx packet format. We analyzed network parameters, such as the sending rate, cover traffic overhead, and mixing delay, to determine the impact of various configurations on the anonymity and performance.

2021

Tools for Analytics and Cognition for Crowd Journalism Application
Scientific paper

Marina Ninoslav,

e-Infrastructure and e-Services for Developing Countries, Springer International Publishing, 2021 , pp.  252-263

Link to the publication

Summary:

Businesses and service consumers should take advantage of social media's ability to adapt their marketing campaigns to achieve a long-term strategic advantage. Setting quantitative and attainable expectations is critical to the progress of every marketing or business endeavour. The development of tools for analytics and cognition (TAC) is essential for customers and providers to increase productivity and inject intelligent insights into operational and mission-critical social media businesses through driven analytics. In this paper, the developed tools provide guided analytics software for intelligent aggregation, cognition and interactive visualization with a monitoring dashboard for concrete crowd journalism use cases. The provider receives an approach to a guided analytic dashboard filled with meaningful business visualization predictions. Among the other things, he can inspect the quantitative metrics for a sharing economy and estimate stakeholders' channel monetization as a new innovative quantified value by engaging users with trusted content. TAC uses this principle of engagement rate measurements and provides visualization insights for stakeholders to choose the right track for boosting their business.

2020

Understanding Calcium-Dependent Conformational Changes in S100A1 Protein: A Combination of Molecular Dynamics and Gene Expression Study in Skeletal Muscle
Scientific paper

Marina Ninoslav, Navaneet Chaturvedi, Ahmad Khurshid, Brijesh Yadav, Eun Jun Lee, Subash Chandra Sonkar, Inho Choi

Cells, 2020 , vol.  9, no  1, pp.  181-201

Link to the publication

Summary:

N. Chaturvedi, K. Ahmad, B. S. Yadav, E. J. Lee, S. C. Sonkar, N. Marina, and
I. Choi. “Understanding Calcium-Dependent Conformational Changes in S100A1
Protein: A Combination of Molecular Dynamics and Gene Expression Study in
Skeletal Muscle”. In: Cells 9.1 (2020). IF: 5.656. issn: 2073-4409. doi: 10.3390/
cells9010181. url: https://www.mdpi.com/2073-4409/9/1/181.

 

 

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