Wyniki wyszukiwania - BazTech

1

Root cause analysis of temporal network faults using echo state networks

Zhang Bixian, Chen Yixia

International Journal of Applied Mathematics and Computer Science

|

2026

|

Vol. 36, no. 1

53--65

EN

The increasing complexity and dynamism of modern networks pose significant challenges for effective fault management. Temporal network faults, characterized by their evolving nature and cascading effects, are particularly difficult to diagnose. Traditional root cause analysis (RCA) methods often struggle with the high dimensionality, non-linearity, and temporal dependencies inherent in network monitoring data. This paper proposes a novel framework, ESN4TRCA - an echo state network for temporal root cause analysis, for identifying the root causes of temporal network faults. ESN4TRCA leverages the inherent capabilities of echo state networks (ESNs), a paradigm of reservoir computing, in modeling complex temporal dynamics with remarkably low training overhead. We formulate the temporal RCA problem as a sequence classification task, where sequences of multivariate key performance indicators (KPIs) and alarm data are mapped to their underlying root causes. The proposed framework encompasses modules for data preprocessing, ESN model construction specifically tailored for heterogeneous network fault data, and a robust inference mechanism. We introduce specific mathematical formulations for the leaky-integrator reservoir dynamics and the output weight training via ridge regression, optimized for the RCA context. Comprehensive experiments are conducted on both a synthetic dataset generated using the NS-3 network simulator and a real-world public dataset. The results demonstrate that ESN4TRCA significantly outperforms state-ofthe- art RCA methods, including traditional machine learning approaches and other recurrent neural network architectures like LSTMs and GRUs, in terms of accuracy, F1-score, and robustness to noise, while maintaining superior computational efficiency. The study highlights the potential of ESNs as a powerful and practical tool for advanced automated network fault management.

2

Data-driven prediction of asphalt pavement temperature profiles

Sudyka Jacek, Mazurek Grzegorz, Buczyński Przemysław

Materiały Budowlane

|

2025

|

nr 12

142--156

EN

Reliable knowledge of the temperature distribution within asphalt pavements is essential for maintenance and structural diagnosis. To forcast the asphalt layers temperature the recurrent neural networks (RNN, including LSTM and BiLSTM) and gradient-boosted decision trees (XGBoost) have been used based on a multi-month field dataset (March – October) with multi-depth temperature measurements and meteorological variables. RNNs captured both diurnal fluctuations and seasonal trends with high predictive accuracy. While the classical XGBoost setup was slightly less precise, it offered very short training times and greater interpretability; its depth-generalized experimental variant enabled interpolation across the full depth range with an error of ~0,97°C (R² ≈ 0,988). The findings support hybridization (RNN + XGBoost) to combine temporal-pattern extraction with efficient regression on static features (e.g., depth, time-of-day).

PL

Utrzymanie i diagnostyka nawierzchni asfaltowych wymagają wiarygodnej informacji o rozkładzie temperatury w czasie i w głąb konstrukcji. Do prognozowania temperatury warstw asfaltowych zastosowano rekurencyjne sieci neuronowe (RNN, w tym LSTM i BiLSTM) oraz gradientowe modele drzew decyzyjnych (XGBoost) na podstawie wielomiesięcznych danych terenowych (marzec – październik) obejmujących pomiary na wielu głębokościach oraz parametry meteorologiczne. Modele RNN wiernie odwzorowały zarówno wahania dobowe, jak i sezonowe. XGBoost, choć w wariancie klasycznym nieco mniej precyzyjny, zapewnił bardzo krótki czas obliczeń i większą interpretowalność; jego wariant eksperymentalny z uogólnieniem po głębokości umożliwił interpolację temperatury w całym zakresie badanych głębokości z błędem rzędu ~0,97°C (R² ≈ 0,988). Wyniki wskazują na zasadność hybrydyzacji podejść (RNN + XGBoost), łączącej identyfikację wzorców czasowych z efektywną regresją po cechach statycznych (m.in. głębokość, pora doby).

3

Intrusion detection with machine learning: a two-step federated approach.using the CIC IoT 2023 dataset

Jakotiya Komal, Shirsath Vishal, Inamdar SharanBasava

Computer Science

|

2025

|

T. 26 (2)

77--95

EN

The main objective of the planned effort is to provide analytical analyses of current intrusion detection systems grounded on ML algorithms. Furthermore, examined in this work are the useful data sets and several techniques already in use to develop an effective IDS using single, hybrid, and ensemble machine learning algorithms. The approaches in the literature have then been investi-gated under several criteria to provide a clear road and direction for the next projects that will be successful. Nowadays, companies of all kinds include an intrusion detection system (IDS), which inhibits cybercrime to protect the network, resources, and private data. Many strategies have been suggested and implemented up till now to prevent uncivil behaviour. Since machine learning (ML) approaches are successful, the proposed approach applied several ML models for the intrusion detection system. The CIC IoT 2023 Dataset is the one applied in this paper, and a two-step process for Intrusion detection was proposed. Tested with several techniques including random forest, XGBoost, logistic regression, MLP model, and RNN. Following fine-tuning, the federated learning model using neural networks had the best accuracy—99.84%.

4

Neural network prediction model : applied to U.S. industrial greenhouse gas emissions

Tseng Shih-Hsien, Wang Chia-Hsuan, Duong Thi Ha Trang

Archives of Environmental Protection

|

2025

|

Vol. 51, no. 1

103--115

EN

This study explores the use of deep learning neural network models for predicting greenhouse gas emissions, focusing on small-sample time-series data sets, an area with limited prior research. It utilizes Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Gated Recurrent Units (GRUs), and Transformers combined with Genetic Algorithms to forecast CO2 emissions from industrial sources in Texas, a major contributor to U.S. greenhouse gas emissions. The analysis is based on the Environmental Protection Agency's (EPA) "Inventory of U.S. Greenhouse Gas Emissions and Sinks" dataset, spanning 1990 to 2020. The results indicate that LSTM and Transformer models are particularly effective, with LSTM outperforming Transformers in computational efficiency by 6.97 times. These findings highlight the potential of LSTM and Transformer models as accurate and stable tools for predicting CO2 emissions in small-sample time-series data, offering valuable insights for future research and policy development in environmental management.

5

Evaluating the forecasting capabilities of probabilistic and point-based LSTM models in sequence prediction

Szetela Paweł, Siwek Krzysztof

Przegląd Elektrotechniczny

|

2024

|

R. 100, nr 5

267--272

EN

his paper compares the performance of probabilistic and non-probabilistic LSTM models in the task of univariate, real valued sequence forecasting. The performance of models is evaluated in terms of mean absolute error and root mean squared error for different forecasting horizons. The results show that probabilistic models can outperform non-probabilistic models in the task of forecasting.

PL

W artykule porównano wydajność probabilistycznych i nieprobabilistycznych modeli LSTM w zadaniu prognozowania szeregów czasowych. Wydajność modeli jest oceniana pod względem średniego błędu bezwzględnego i błędu średniokwadratowego dla różnych horyzontów prognozy. Wyniki pokazują, że modele probabilistyczne mogą przewyższać modele nieprobabilistyczne w zadaniu prognozowania.

6

Efficiency of artificial intelligence methods for hearing loss type classification : an evaluation

Kassjański Michał, Kulawiak Marcin, Przewoźny Tomasz, Tretiakow Dmitry, Kuryłowicz Jagoda, Molisz Andrzej, Koźmiński Krzysztof, Kwaśniewska Aleksandra, Mierzwińska-Dolny Paulina, Grono Miłosz

Journal of Automation Mobile Robotics and Intelligent Systems

|

2024

|

Vol. 18, No. 3

28--38

EN

The evaluation of hearing loss is primarily conducted by pure tone audiometry testing, which is often regarded as golden standard for assessing auditory function. If the presence of hearing loss is determined, it is possible to differentiate between three types of hearing loss: sensorineural, conductive, and mixed. This study presents a comprehensive comparison of a variety of AI classification models, performed on 4007 pure tone audiometry samples that have been labeled by professional audiologists in order to develop an automatic classifier of hearing loss type. The tested models include Logistic Regression, Support Vector Machines, Stochastic Gradient Descent, Decision Trees, Random Forest, Feedforward Neural Network (FNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The presented work also investigates the influence of training dataset augmentation with the use of a Conditional Generative Adversarial Network on the performance of machine learning algorithms and examines the impact of various standardization procedures on the effectiveness of deep learning architectures. Overall, the highest classification performance, was achieved by LSTM with an out-of-training accuracy of 97.56%.

7

Influence of modelling phase transformations with the use of LSTM network on the accuracy of computations of residual stresses for the hardening process

Wróbel Joanna, Kulawik Adam

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2023

|

Vol. 71, nr 4

art. no. e145681

EN

Replacing mathematical models with artificial intelligence tools can play an important role in numerical models. This paper analyses the modeling of the hardening process in terms of temperature, phase transformations in the solid state and stresses in the elastic-plastic range. Currently, the use of artificial intelligence tools is increasing, both to make greater generalizations and to reduce possible errors in the numerical simulation process. It is possible to replace the mathematical model of phase transformations in the solid state with an artificial neural network (ANN). Such a substitution requires an ANN network that converts time series (temperature curves) into shares of phase transformations with a small training error. With an insufficient training level of the network, significant differences in stress values will occur due to the existing couplings. Long-Short-Term Memory (LSTM) networks were chosen for the analysis. The paper compares the differences in stress levels with two coupled models using a macroscopic model based on CCT diagram analysis and using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) and Koistinen-Marburger (KM) equations, against the model memorized by the LSTM network. In addition, two levels of network training accuracy were also compared. Considering the results obtained from the model based on LSTM networks, it can be concluded that it is possible to effectively replace the classical model in modeling the phenomena of the heat treatment process.

8

Attention-based deep learning model for Arabic handwritten text recognition

Aïcha Gader Takwa Ben, Echi Afef Kacem

Machine Graphics and Vision

|

2022

|

Vol. 31, No. 1/4

49--73

EN

This work proposes a segmentation-free approach to Arabic Handwritten Text Recog-nition (AHTR): an attention-based Convolutional Neural Network - Recurrent Neural Network - Con-nectionist Temporal Classification (CNN-RNN-CTC) deep learning architecture. The model receives asinput an image and provides, through a CNN, a sequence of essential features, which are transferred toan Attention-based Bidirectional Long Short-Term Memory Network (BLSTM). The BLSTM gives features sequence in order, and the attention mechanism allows the selection of relevant information from the features sequences. The selected information is then fed to the CTC, enabling the loss calculation and the transcription prediction. The contribution lies in extending the CNN by dropout layers, batch normalization, and dropout regularization parameters to prevent over-fitting. The output of the RNN block is passed through an attention mechanism to utilize the most relevant parts of the input sequence in a flexible manner. This solution enhances previous methods by improving the CNN speed and performance and controlling over model over-fitting. The proposed system achieves the best accuracy of97.1% for the IFN-ENIT Arabic script database, which competes with the current state-of-the-art. It was also tested for the modern English handwriting of the IAM database, and the Character Error Rate of 2.9% is attained, which confirms the model’s script independence.

9

Robust zeroing neural networks with two novel power-versatile activation functions for solving dynamic Sylvester equation

Zhou Peng, Tan Mingtao

Bulletin of the Polish Academy of Sciences. Technical Sciences

|

2022

|

Vol. 70, nr 3

art. no. e141307

EN

In this work, two robust zeroing neural network (RZNN) models are presented for online fast solving of the dynamic Sylvester equation (DSE), by introducing two novel power-versatile activation functions (PVAF), respectively. Differing from most of the zeroing neural network (ZNN) models activated by recently reported activation functions (AF), both of the presented PVAF-based RZNN models can achieve predefined time convergence in noise and disturbance polluted environment. Compared with the exponential and finite-time convergent ZNN models, the most important improvement of the proposed RZNN models is their fixed-time convergence. Their effectiveness and stability are analyzed in theory and demonstrated through numerical and experimental examples.

10

Predicting hospital emergency department visits with deep learning approaches

Zhao Xinxing, Lai Joel Weijia, Ho Andrew Fu Wah, Liu Nan, Ong Marcus Eng Hock, Cheong Kang Hao

Biocybernetics and Biomedical Engineering

|

2022

|

Vol. 42, no. 3

1051--1065

EN

Overcrowding in emergency department (ED) causes lengthy waiting times, reduces adequate emergency care and increases rate of mortality. Accurate prediction of daily ED visits and allocating resources in advance is one of the solutions to ED overcrowding problem. In this paper, a deep stacked architecture is being proposed and applied to the daily ED visits prediction problem with deep components such as Long Short Term Memory (LSTM), Gated Recurrent Units (GRU) and simple Recurrent Neural Network (RNN). The proposed architecture achieves very high mean accuracy level (94.28–94.59%) in daily ED visits predictions. We have also compared the performance of this architecture with non-stacked deep models and traditional prediction models. The results indicate that deep stacked models outperform (4–7%) the traditional prediction models and other non-stacked deep learning models (1–2%) in our prediction tasks. The application of deep neural network in ED visits prediction is novel as this is one of the first studies to apply a deep stacked architecture in this field. Importantly, our models have achieved better prediction accuracy (in one case comparable) than the state-of-the-art in the literature.

11

Traffic flow prediction in inland waterways of Assam region using uncertain spatiotemporal correlative features

Muthukumaran Venkatesan, Natarajan Rajesh, Kaladevi Amarakundhi Chandrasekaran, Magesh Gopu, Babu Swapna

Acta Geophysica

|

2022

|

Vol. 70, no 6

2979--2990

EN

Modern civilization has reported a significant rise in the volume of traffic on inland rivers all over the globe. Traffic flow prediction is essential for a good travel experience, but adequate computer processes for processing unpredictable spatiotemporal data (timestamp, weather, vessel_ID, water level, vessel_position, vessel_speed) in the inland water transportation industry are lacking. Moreover, such type of prediction relies primarily on past traffic patterns and perhaps other pertinent facts. Thus, we propose a deep learning-based computing process, namely Convolution Neural Network-Long Short-Term Memory Network (CNN-LSTM), a progressive predictor of employing uncertain spatiotemporal information to decrease navigation mishaps, traffic and flow prediction failures during transportation. Spatiotemporal correlation of current traffic flow may be processed using a simplified CNN-LSTM model. This hybridized prediction technique decreases update costs and meets the prediction needs with minimal computing overhead. A short case study on the waterways of the Indian state of Assam from Sandiya (27.835090 latitude, 95.658590 longitude) to Dhubri (26.022699 latitude, 89.978401 longitude) is undertaken to assess the model's performance. The evaluation of the suggested method includes a variety of trajectories of water transportation vehicles, including ferries, sailing boats, container ships, etc. The suggested approach outperforms conventional traffic flow predicting methods when it comes to short-term prediction with minimal predictive error (<2.75) and exhibited a major difference of more than 45% on the comparison of other methods.

12

An optimized parallel implementation of non-iteratively trained recurrent neural networks

El Zini Julia, Rizk Yara, Awad Mariette

Journal of Artificial Intelligence and Soft Computing Research

|

2021

|

Vol. 11, No. 1

33--50

EN

Recurrent neural networks (RNN) have been successfully applied to various sequential decision-making tasks, natural language processing applications, and time-series predictions. Such networks are usually trained through back-propagation through time (BPTT) which is prohibitively expensive, especially when the length of the time dependencies and the number of hidden neurons increase. To reduce the training time, extreme learning machines (ELMs) have been recently applied to RNN training, reaching a 99% speedup on some applications. Due to its non-iterative nature, ELM training, when parallelized, has the potential to reach higher speedups than BPTT. In this work, we present Opt-PR-ELM, an optimized parallel RNN training algorithm based on ELM that takes advantage of the GPU shared memory and of parallel QR factorization algorithms to efficiently reach optimal solutions. The theoretical analysis of the proposed algorithm is presented on six RNN architectures, including LSTM and GRU, and its performance is empirically tested on ten time-series prediction applications. Opt- PR-ELM is shown to reach up to 461 times speedup over its sequential counterpart and to require up to 20x less time to train than parallel BPTT. Such high speedups over new generation CPUs are extremely crucial in real-time applications and IoT environments.

13

Forecasting future values of time series using the lstm network on the example of currencies and WIG20 companies

Mróz Bartosz, Nowicki Filip

Zeszyty Naukowe. Telekomunikacja i Elektronika / Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy

|

2020

|

nr 24

13--30

EN

The article presents a comparison of the RNN, GRU and LSTM networks in predicting future values of time series on the example of currencies and listed companies. The stages of creating an application which is a implementation of the analyzed issue were also shown – the selection of networks, technologies, selection of optimal network parameters. Additionally, two conducted experiments were discussed. The first was to predict the next values of WIG20 companies, exchange rates and cryptocurrencies. The second was based on investments in cryptocurrencies guided solely by the predictions of artificial intelligence. This was to check whether the investments guided by the predictions of such a program have a chance of effective earnings. The discussion of the results of the experiment includes an analysis of various interesting phenomena that occurred during its duration and a comprehensive presentation of the relatively high efficiency of the proposed solution, along with all kinds of graphs and comparisons with real data. The difficulties that occurred during the experiments, such as coronavirus or socio-economic events, such as riots in the USA, were also analyzed. Finally, elements were proposed that should be improved or included in future versions of the solution – taking into account world events, market anomalies and the use of supervised learning.

PL

W artykule przedstawiono porównanie sieci RNN, GRU i LSTM w przewidywaniu przyszłych wartości szeregów czasowych na przykładzie walut i spółek giełdowych. Przedstawiono również etapy tworzenia aplikacji będącej realizacją analizowanego zagadnienia – dobór sieci, technologii, dobór optymalnych parametrów sieci. Dodatkowo omówiono dwa przeprowadzone eksperymenty. Pierwszym było przewidywanie kolejnych wartości spółek z WIG20, kursów walut i kryptowalut. Drugi opierał się na inwestycjach w kryptowaluty, kierując się wyłącznie przewidywaniami sztucznej inteligencji. Miało to na celu sprawdzenie, czy inwestowanie na podstawie przewidywania takiego programu pozwala na efektywne zarobki. Omówienie wyników eksperymentu obejmuje analizę różnych ciekawych zjawisk, które wystąpiły w czasie jego trwania oraz kompleksowe przedstawienie relatywnie wysokiej skuteczności proponowanego rozwiązania wraz z wszelkiego rodzaju wykresami i porównaniami z rzeczywistymi danymi. Analizowano również trudności, które wystąpiły podczas eksperymentów, takie jak koronawirus, wydarzenia społeczno-gospodarcze czy zamieszki w USA. Na koniec zaproponowano elementy, które należałoby ulepszyć lub uwzględnić w przyszłych wersjach rozwiązania, uwzględniając wydarzenia na świecie, anomalie rynkowe oraz wykorzystanie uczenia się nadzorowanego.

14

Shallow, Deep, Ensemble models for Network Device Workload Forecasting

Liu Cenru

Annals of Computer Science and Information Systems

|

2020

|

Vol. 21

101--104

EN

Reliable prediction of workload-related characteristics of monitored devices is important and helpful for management of infrastructure capacity. This paper presents 3 machine learning models (shallow, deep, ensemble) with different complexity for network device workload forecasting. The performance of these models have been compared using the data provided in FedCSIS'20 Challenge. The R2 scores achieved from the cascade Support Vector Regression (SVR) based shallow model, Long short-term memory (LSTM) based deep model, and hierarchical linear weighted ensemble model are 0.2506, 0.2831, and 0.3059, respectively, and was ranked 3rd place in the preliminary stage of the challenges.

15

Future Graduate Salaries Prediction Model Based On Recurrent Neural Network

Siłka Jakub, Wieczorek Michał, Woźniak Marcin

Annals of Computer Science and Information Systems

|

2020

|

Vol. 21

427--430

EN

Prediction models are widely applied in several fields. In this study we present a discussion on using Recurrent Neural Network as predictor for salaries of future graduates. The model is based on feature analysis which leads to input values of the predictor. We have analyzed several compositions and ideas. As a result we have selected Recurrent Neural Network to be the most accurate. Presented results confirm this selection and show high precision.

16

An overview of deep learning techniques for short-term electricity load forecasting

Adewuyi Saheed, Aina Segun, Uzunuigbe Moses, Lawal Aderonke, Oluwaranti Adeniran

Applied Computer Science

|

2019

|

Vol. 15, no. 4

75--92

EN

This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems.

17

A class of neuro-computational methods for assamese fricative classification

Patgiri C., Sarma M., Sarma K. K.

Journal of Artificial Intelligence and Soft Computing Research

|

2015

|

Vol. 5, No. 1

59--70

EN

In this work, a class of neuro-computational classifiers are used for classification of fricative phonemes of Assamese language. Initially, a Recurrent Neural Network (RNN) based classifier is used for classification. Later, another neuro fuzzy classifier is used for classification. We have used two different feature sets for the work, one using the specific acoustic-phonetic characteristics and another temporal attributes using linear prediction cepstral coefficients (LPCC) and a Self Organizing Map (SOM). Here, we present the experimental details and performance difference obtained by replacing the RNN based classifier with an adaptive neuro fuzzy inference system (ANFIS) based block for both the feature sets to recognize Assamese fricative sounds.

18

Recurrent Neural Networks for Predictive Maintenance of Mill Fan Systems

Koprinkova-Hristova P. D., Hadjiski M. B., Doukovska L. A., Beloreshki S. V.

International Journal of Electronics and Telecommunications

|

2011

|

Vol. 57, No. 3

401-406

EN

In the present paper we focus on online monitoring system for predictive maintenance based on sensor automated inputs. Our subject was a device from Maritsa East 2 power plant - a mill fan. The main sensor information we have access to is based on the vibration of the nearest to the mill rotor bearing block. Our aim was to create a (nonlinear) model able to predict on time possible changes in vibrations tendencies that can be early signal for system work deterioration. For that purpose, we compared two types of recurrent neural networks: historical Elman architecture and a recently developed kind of RNN named Echo stet networks (ESN). The preliminary investigations showed better approximation and faster training abilities of ESN in comparison to the Elman network. Direction of future work will be increasing of predications time horizon and inclusion of our predictor at lower level of a complex predictive maintenance system.