Wyniki wyszukiwania - BazTech

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Towards Industry 4.0: Machine malfunction prediction based on IIoT streaming data

Nikolova Dragana, Lameski Petre, Pires Ivan Miguel, Zdravevski Eftim

Annals of Computer Science and Information Systems

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2023

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Vol. 35

291--296

EN

The manufacturing industry relies on continuous optimization to meet quality and safety standards, which is part of the Industry 4.0 concept. Predicting when a specific part of a product will fail to meet these standards is of utmost importance and requires vast amounts of data, which often is collected from variety of sensors, often reffered to as Industrial Internet of Things (IIoT). Using a published dataset from Bosch, that describes the process at every step of production, we aim to train a machine learning model that can accurately predict faults in the manufacturing process. The dataset provides two years of production data across four production lines and 52 stations. Considering that the data generated from each production part includes 4,264 features, we investigate various feature selection and data preprocessing methods. The obtained results exhibit AUC ROC of up to 0.997, which is remarkable and promising even for real-life production use.

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Application of Diversified Ensemble Learning in Real-life Business Problems: The Case of Predicting Costs of Forwarding Contracts

Trajanoska Milena, Gjorgovski Pavel, Zdravevski Eftim

Annals of Computer Science and Information Systems

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2022

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Vol. 30

437--446

EN

Finding an optimal machine learning model that can be applied to a business problem is a complex challenge that needs to provide a balance between multiple requirements, including a high predictive performance of the model, continuous learning and deployment, and explainability of the predictions. The topic of the FedCSIS 2022 Challenge: ‘Predicting the Costs of Forwarding Contracts' is related to the challenges logistics and transportation companies are facing. To tackle this challenge, we utilized the provided datasets to establish an entire Machine Learning framework which includes domain-specific feature engineering and enrichment, generic feature transformation and extraction, model hyper-parameter tuning, and creating ensembles of traditional and deep learning models. Our contributions additionally include an analysis of the types of models which are suitable for the case of predicting a multimodal continuous target variable, as well as explainable analysis of the features which have the largest impact on predicting the value of these costs. We further show that ensembles created by combining multiple different models trained with different algorithms can improve the performance on unseen data. In this particular dataset, the experiments showed that such a combination improves the score by 3% compared to the best performing individual model.

3

Short-term air pollution forecasting based on environmental factors and deep learning models

Arsov Mirche, Zdravevski Eftim, Lameski Petre, Corizzo Roberto, Koteli Nikola, Mitreski Kosta, Trajkovik Vladimir

Annals of Computer Science and Information Systems

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2020

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Vol. 21

15--22

EN

The effects of air pollution on people, the environment, and the global economy are profound - and often under-recognized. Air pollution is becoming a global problem. Urban areas have dense populations and a high concentration of emission sources: vehicles, buildings, industrial activity, waste, and wastewater. Tackling air pollution is an immediate problem in developing countries, such as North Macedonia, especially in larger urban areas. This paper exploits Recurrent Neural Network (RNN) models with Long Short-Term Memory units to predict the level of PM10 particles in the near future (+3 hours), measured with sensors deployed in different locations in the city of Skopje. Historical air quality measurements data were used to train the models. In order to capture the relation of air pollution and seasonal changes in meteorological conditions, we introduced temperature and humidity data to improve the performance. The accuracy of the models is compared to PM10 concentration forecast using an Autoregressive Integrated Moving Average (ARIMA) model. The obtained results show that specific deep learning models consistently outperform the ARIMA model, particularly when combining meteorological and air pollution historical data. The benefit of the proposed models for reliable predictions of only 0.01 MSE could facilitate preemptive actions to reduce air pollution, such as temporarily shutting main polluters, or issuing warnings so the citizens can go to a safer environment and minimize exposure.

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Explorations into Deep Learning Text Architectures for Dense Image Captioning

Toshevska Martina, Stojanovska Frosina, Zdravevski Eftim, Lameski Petre, Gievska Sonja

Annals of Computer Science and Information Systems

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2020

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Vol. 21

129--136

EN

Image captioning is the process of generating a textual description that best fits the image scene. It is one of the most important tasks in computer vision and natural language processing and has the potential to improve many applications in robotics, assistive technologies, storytelling, medical imaging and more. This paper aims to analyse different encoder-decoder architectures for dense image caption generation while focusing on the text generation component. Already trained models for image feature generation are utilized with transfer learning. These features are used for describing the regions using three different models for text generation. We propose three deep learning architectures for generating one-sentence captions of Regions of Interest (RoIs). The proposed architectures reflect several ways of integrating features from images and text. The proposed models were evaluated and compared with several metrics for natural language generation.