Month: November 2022

Big Data Analitic Tools

Big Data Analitic Tools

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Unlocking new insights from complex, vast, diverse, and massive quantities of data to accelerate digital transformation, innovation, and social sustainability is the primary objective of Big Data Analytics. It uses a palette of advanced techniques and approaches from emerging fields such as Data Mining, Machine Learning and Deep Learning for extracting valuable information. Various tools support and optimize knowledge discovery engineering.

In such a context, the short-term training aims to broaden the students’ interest in data-driven projects as a novel paradigm in Industry 4.0 and Society 5.0, by answering a series of questions:
 What acts in the Big Data value-chain paradigm?
 What are the key technologies behind Big Data Analytics (BDA)?
 What tools and software enable meaningful insight from big data?
 What tools and software can be used for BDA with no or minimal coding skills?

Besides these questions, the training provides practical guides to particular tools that support and automate end-to-end data analytics, from data collection, cleansing, and transformation through model building, evaluation, and tuning, to visualization and communication of results.

The training is competences oriented utilizing learning-by-doing and case-based methods. The course is offered to computer science students from University of Nis (Serbia), University of Library Studies and Information Technologies (Bulgaria) and University of Bielsko-Biala (Poland).

All information about this event can be found in 2022_Scientific Study_iBigWorld.

Article: Ensemble of RNN classifiers for activity detection using smartphone and supporting nodes

Article: Ensemble of RNN classifiers for activity detection using smartphone and supporting nodes

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The dataset was provided for classification of activity using sensors placed in four places: waist, chest, arm and leg.

The dataset contains eight classes:

  • walking in normal paste, where legs and arms are moving without any luggage,
  • jogging in moderate paste, where limb movement is faster, and the body is moving more dynamically,
  • squats, where a person is performing squats in place and arms holding a balance,
  • jump, where a person jumps in place as a part of aerobic exercises,
  • laying, where a person is lying on his back, and major movement corresponds to chest breathing,
  • arms swing, where the arms are moving during the exercise or housework,
  • siting, where the person sits at the desk and performs simple writing (office) activities.
  • standing in place, where only natural body balance can be registered.

The repository can be found in: https://github.com/drOneMB/activity-detection