Insights into energy indicators analytics towards European green energy transition using statistics and self-organizing maps

IEEE Access

Published On 2021/4/23

The more frequent meteorological anomalies and climate changes push us to consider green sustainable energy as a chance to slow down such issues. Thus, we should introspect the correlations between indicators over time and understand the underneath of their meaning. Large volumes of data regarding energy are provided by Eurostat and other official data sources that require data analytics to extract valuable insights from energy indicators and indices to better understand the dynamics towards a green energy transition of the European Union State Members (EU-SM). In this paper, we analyze several energy indicators calculated for a 12-year time span with statistics and machine learning techniques, such as an unsupervised clustering algorithm with Self-Organizing Maps (SOM). Grouping the EU-SM by energy indicators from the beginning years to the end of the analyzed interval reveals differences and …

Journal

IEEE Access

Published On

2021/4/23

Volume

9

Page

64427-64444

Authors

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Position

The

H-Index(all)

19

H-Index(since 2020)

17

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

data mining

energy

Nancu Dumitru

Nancu Dumitru

Universitatea Ovidius Constanta

Position

H-Index(all)

9

H-Index(since 2020)

7

I-10 Index(all)

0

I-10 Index(since 2020)

0

Citation(all)

0

Citation(since 2020)

0

Cited By

0

Research Interests

small and medium enterprisess

economy

finance

Guarantee

intelligence

University Profile Page

Dorel Dusmanescu

Dorel Dusmanescu

Universitatea Petrol-Gaze din Ploiesti

Position

Full Professor

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9

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6

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0

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0

Citation(since 2020)

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0

Research Interests

computer science

databases

energy economics

Cristian Bucur

Cristian Bucur

Universitatea Petrol-Gaze din Ploiesti

Position

Lecturer Phd Romania

H-Index(all)

7

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5

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0

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0

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0

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0

Research Interests

machine learning

sentiment analysis

big data

nosql

web mining

Bogdan Tudorica

Bogdan Tudorica

Universitatea Petrol-Gaze din Ploiesti

Position

H-Index(all)

7

H-Index(since 2020)

6

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0

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Research Interests

Computer Science

Teaching Methodology

eLearning

Other Articles from authors

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Iscience

A value sharing method for heterogeneous energy communities archetypes

A novel value sharing (VS) method is proposed that distributes the energy communities (ECs) value based on the individual contribution to the total surplus/deficit. It considers the load-generation profile of each EC member and allocates a higher share to members who contribute to the EC revenue. The lowest share is received by the members with the highest demand that has to be supplied from the shared generation or from the grid, contributing to the EC cost. Several allocation methods are compared using the fairness index (FI), and, for setting the strategy of the EC using a decision model, as the strategy may vary over time, an objective function is defined as a combination between FI and self-sufficiency index using weighting coefficients. The methodology is implemented as an algorithm that automatically calculates and distributes the gain. For the proposed VS method, the FI is between 0.81 and 1.

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Journal of Theoretical and Applied Electronic Commerce Research

The Impact of Academic Publications over the Last Decade on Historical Bitcoin Prices Using Generative Models

Since 2012, researchers have explored various factors influencing Bitcoin prices. Up until the end of July 2023, more than 9100 research papers on cryptocurrencies were published and indexed in the Web of Science Clarivate platform. The objective of this paper is to analyze the impact of publications on Bitcoin prices. This study aims to uncover significant themes within these research articles, focusing on cryptocurrencies in general and Bitcoin specifically. The research employs latent Dirichlet allocation to identify key topics from the unstructured abstracts. To determine the optimal number of topics, perplexity and topic coherence metrics are calculated. Additionally, the abstracts are processed using BERT-transformers and Word2Vec and their potential to predict Bitcoin prices is assessed. Based on the results, while the research helps in understanding cryptocurrencies, the potential of academic publications to influence Bitcoin prices is not significant, demonstrating a weak connection. In other words, the movements of Bitcoin prices are not influenced by the scientific writing in this specific field. The primary topics emerging from the analysis are the blockchain, market dynamics, transactions, pricing trends, network security, and the mining process. These findings suggest that future research should pay closer attention to issues like the energy demands and environmental impacts of mining, anti-money laundering measures, and behavioral aspects related to cryptocurrencies.

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Investigating the relationship between macroeconomic indicators, renewables and pollution across diverse regions in the globalization era

The aim of this study is to determine the long-term relationship between growth rate of stock index cash returns (TEDIX) and a set of MACROECONOMIC VARIABLES such as inflation rate (CPI), money supply growth rates, exchange rates and oil revenues. The data in this study has been analyzed as seasonal for the period of 1998 to 2007 with Auto Regressive Distributed Lag (ARDL) method. The Results of Dickey-Fuller (ADF) generalized unit root test showed that variable rate liquidity in first order and other variables in the first order of difference are stable. The co-integration test results also indicated there is a long-term relationship between aforementioned economic variables and growth rate cash index returns, so that the relation between the long-term growth and cash returns index of oil revenue and exchange rate is negative and with inflation this relationship will be positive. Besides the significant liquidity growth factor in the ninety percent level of confidence was rejected.

Cristian Bucur

Cristian Bucur

Universitatea Petrol-Gaze din Ploiesti

Utilities Policy

Energetic Equilibrium: Optimizing renewable and non-renewable energy sources via particle swarm optimization

This study introduces a novel approach utilizing Particle Swarm Optimization (PSO) to determine the optimal mix of renewable and non-renewable energy sources specifically tailored for Romania. Relying on data from January 2019 to August 2022, this research ascertains the most efficient combination of energy sources, considering cost, generation capacity, and environmental impact. PSO is compared with six other optimization algorithms: grey wolf optimizer, genetic algorithm, harmony search, mayfly algorithm, flower pollination algorithm, and CUKO search. The following mix is proposed: coal 0 %, oil & gas 2.74 %, hydro 18.92 %, nuclear 12.20 %, wind 24.05 %, solar 26.99 %, and biomass 15.09 %.

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

International Journal of Computational Intelligence Systems

Predicting Day-Ahead Electricity Market Prices through the Integration of Macroeconomic Factors and Machine Learning Techniques

Several events in the last years changed to some extent the common understanding of the electricity day-ahead market (DAM). The shape of the electricity price curve has been altered as some factors that underpinned the electricity price forecast (EPF) lost their importance and new influential factors emerged. In this paper, we aim to showcase the changes in EPF, understand the effects of uncertainties and propose a forecasting method using machine learning (ML) algorithms to cope with random events such as COVID-19 pandemic and the conflict in Black Sea region. By adjusting the training period according to the standard deviation that reflects the price volatility, feature engineering and by using two regressors for weighing the results, significant improvements in the performance of the EPF are achieved. One of the contributions of the proposed method consists in adjusting the training period considering the …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Technological and Economic Development of Economy

Improving the strategies of the market players using an AI-powered price forecast for electricity market

This paper analyses the recent evolution of the electricity price of one of the East-European countries’ Balancing Markets (BM)–Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants’ trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players.

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Kybernetes

An analysis of the geopolitical and economics influence on tourist arrivals in Russia using a nonlinear autoregressive distributed lag model

PurposeThe COVID-19 pandemic and the onset of the conflict in Ukraine led to a sustained downturn in tourist arrivals (TA) in Russia. This paper aims to explore the influence of geopolitical risk (GPR) and other indices on TA over 1995–2023.Design/methodology/approachWe employ a nonlinear autoregressive distributed lag (NARDL) model to analyze the effects, capturing both the positive and negative shocks of these variables on TA.FindingsOur research demonstrates that the NARDL model is more effective in elucidating the complex dynamics between macroeconomic factors and TA. Both an increase and a decrease in GPR lead to an increase in TA. A 1% negative shock in GPR leads to an increase in TA by 1.68%, whereas a 1% positive shock in GPR also leads to an increase in TA by 0.5%. In other words, despite the increase in GPR, the number of tourists coming to Russia increases by 0.5% for every 1 …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Knowledge and Information Systems

On-grid and off-grid photovoltaic systems forecasting using a hybrid meta-learning method

In this paper, we investigate two types of photovoltaic (PV) systems (on-grid and off-grid) of different sizes and propose a reliable PV forecasting method. The novelty of our research consists in a weather data-driven feature engineering considering the operation of the PV systems in similar conditions and merging the results of deterministic and stochastic models, namely Machine Learning algorithms (Random Forest—RF, eXtreme Gradient Boost—XGB) and Deep Learning algorithms (Deep Neural Networks—DNN, Gated Recurrent Unit—GRU) into a Hybrid Meta-learning Forecasting method. It combines the estimations of the above-mentioned algorithms with relevant features to predict the PV output using a Long Short-Term Memory model. To design the PV forecast for off-grid systems, that are equally important for prosumers, and approximate the potential power of these systems, the level of load and charging …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

IEEE Access

Exploring the Dynamics of Brent Crude Oil, S&P500 and Bitcoin Prices Amid Economic Instability

In this paper, we mainly investigate three variables from the price volatility point of view: Brent crude oil, S&P500 and Bitcoin (BTCUSD), aiming to underline the impact of price volatility. Brent crude oil accounts for two-thirds of the oil market. Its price volatility has a significant impact on environmental, transportation, mobility, economic and social aspects that affect sustainability. This paper conducts an extensive examination of the forecasting capabilities of various GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models, identifying the most suitable GARCH model for estimating Value at Risk (VaR) for Brent crude oil price. The assessment of VaR for different GARCH models is carried out using Kupiec’s Probability of Failure (POF) test and Christoffersen’s test. This study leverages Brent crude oil data spanning from 2019 to 2023. Additionally, to prove the robustness of the GARCH models, we further …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Electrical Engineering

Electricity price forecast on day-ahead market for mid-and short terms: capturing spikes in data sequences using recurrent neural network techniques

This paper aims to forecast the electricity prices in the day-ahead market (DAM) with complex recurrent neural networks (RNNs), which are powerful in predicting the sequential prices with lags of unknown duration between significant peaks in the price curve. Recently, the electricity markets have been shaken by random events, such as the COVID-19 pandemic or the conflict in Ukraine. Therefore, long short-term memory (LSTM), Gated Recurrent Unit (GRU) and echo state networks (ESNs) are more appropriate for memorizing random events that must be remembered after some time to adequately enhance the mid-and short-run forecast. Both methods overcome the vanishing gradient problem that is common for RNN using memory cells and gates that allow the updating of the memory and tracking long-term dependencies in the input sequence. Several time series prices from neighboring East European …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

American Journal of Economics and Sociology

Are skepticism and moderation dominating attitudes toward AI‐based technologies?

AI advancements are poised to substantially modify human abilities in the foreseeable future. They include the integration of Brain–Computer Interfaces (BCIs) to augment cognitive functions, the application of gene editing, and the utilization of AI‐powered robotic exoskeletons to enhance physical strength. This study employs a comprehensive analytical framework combining factor analysis, clustering, ANOVA, and logistic regression to investigate public attitudes toward these transformative technologies. Our findings reveal three distinct clusters of public opinion reflecting varying optimism and concern toward AI technologies. Cluster 1 (1574 participants) held a positive view with high excitement while Cluster 2 (1334 participants) showed a balanced stance. Cluster 3 (2199 participants) expressed heightened concern despite some excitement. Notably, regional disparities, particularly between urban and rural …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Electronic Commerce Research

Is Bitcoin ready to be a widespread payment method? Using price volatility and setting strategies for merchants

Bitcoin has gradually gained acceptance as a payment method that, unlike electronic payments in dollars or euros, passes through the international trading system with zero or lower fees. Moreover, Bitcoin and e-commerce have become increasingly intertwined in recent years as cryptocurrencies gain mainstream acceptance. In this paper, we analyze Bitcoin price evolution from September 2014 until July 2023, factors that influence price volatility and assess its future volatility using Autoregressive Conditional Heteroskedasticity (ARCH) models that predict the volatility of financial returns to conceive strategies for merchants that accept Bitcoin as a payment option. The Generalized ARCH model (GARCH) extends the model to capture more persistent volatility patterns. Further, we estimate symmetric and asymmetric GARCH (1,1)-type models with normal and non-normal innovations. The best proved to be EGARCH …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Utilities Policy

Energetic Equilibrium: Optimizing renewable and non-renewable energy sources via particle swarm optimization

This study introduces a novel approach utilizing Particle Swarm Optimization (PSO) to determine the optimal mix of renewable and non-renewable energy sources specifically tailored for Romania. Relying on data from January 2019 to August 2022, this research ascertains the most efficient combination of energy sources, considering cost, generation capacity, and environmental impact. PSO is compared with six other optimization algorithms: grey wolf optimizer, genetic algorithm, harmony search, mayfly algorithm, flower pollination algorithm, and CUKO search. The following mix is proposed: coal 0 %, oil & gas 2.74 %, hydro 18.92 %, nuclear 12.20 %, wind 24.05 %, solar 26.99 %, and biomass 15.09 %.

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

International Journal of Computational Intelligence Systems

Machine Learning Algorithms for Power System Sign Classification and a Multivariate Stacked LSTM Model for Predicting the Electricity Imbalance Volume

The energy transition to a cleaner environment has been a concern for many researchers and policy makers, as well as communities and non-governmental organizations. The effects of climate change are evident, temperatures everywhere in the world are getting higher and violent weather phenomena are more frequent, requiring clear and firm pro-environmental measures. Thus, we will discuss the energy transition and the support provided by artificial intelligence (AI) applications to achieve a cleaner and healthier environment. The focus will be on applications driving the energy transition, the significant role of AI, and collective efforts to improve societal interactions and living standards. The price of electricity is included in almost all goods and services and should be affordable for the sustainable development of economies. Therefore, it is important to model, anticipate and understand the trend of electricity …

Bogdan Tudorica

Bogdan Tudorica

Universitatea Petrol-Gaze din Ploiesti

Utilities Policy

Energetic Equilibrium: Optimizing renewable and non-renewable energy sources via particle swarm optimization

This study introduces a novel approach utilizing Particle Swarm Optimization (PSO) to determine the optimal mix of renewable and non-renewable energy sources specifically tailored for Romania. Relying on data from January 2019 to August 2022, this research ascertains the most efficient combination of energy sources, considering cost, generation capacity, and environmental impact. PSO is compared with six other optimization algorithms: grey wolf optimizer, genetic algorithm, harmony search, mayfly algorithm, flower pollination algorithm, and CUKO search. The following mix is proposed: coal 0 %, oil & gas 2.74 %, hydro 18.92 %, nuclear 12.20 %, wind 24.05 %, solar 26.99 %, and biomass 15.09 %.

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Energy Policy

Enabling coordination in energy communities: A Digital Twin model

Starting from the EU vision for Energy Communities (EC), our purpose is to support them by proposing a Digital Twin (DT) that includes a bi-level optimization model to deliver coordination, economic, social, and environmental benefits to its members that can be quantified as Key Performance Indicators (KPI). The diversity of EC members from the size and interest perspectives leads us to consider a bi-level optimization model. It offers support to individual consumers/prosumers (first level) and coordination for EC (second level). This model is embedded into a DT that replicates the EC and the operation of individual entities such as consumers/prosumers and public assets. The DT is created as an automatic assistant with two components: iEMS – as a member's assistant and eEMS – as an EC assistant. These components optimize the schedule, generate bids for the Local Electricity Market (LEM) and control the …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Journal of Forecasting

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning

The creation of features makes the difference in improving the photovoltaic forecast (PVF) for on‐grid, hybrid and off‐grid PV systems. The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)‐based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Humanities and Social Sciences Communications

Exploring excitement counterbalanced by concerns towards AI technology using a descriptive-prescriptive data processing method

Given the current pace of technological advancement and its pervasive impact on society, understanding public sentiment is essential. The usage of AI in social media, facial recognition, and driverless cars has been scrutinized using the data collected by a complex survey. To extract insights from data, a descriptive-prescriptive hybrid data processing method is proposed. It includes graphical visualization, cross-tabulation to identify patterns and correlations, clustering using K-means, principal component analysis (PCA) enabling 3D cluster representation, analysis of variance (ANOVA) of clusters, and forecasting potential leveraged by Random Forest to predict clusters. Three well-separated clusters with a silhouette score of 0.828 provide the profile of the respondents. The affiliation of a respondent to a particular cluster is assessed by an F1 score of 0.99 for the test set and 0.98 for the out-of-sample set. With over …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Engineering Applications of Artificial Intelligence

An ensemble learning method for Bitcoin price prediction based on volatility indicators and trend

Predicting the price of Bitcoin poses a challenge for researchers, merchants, traders and investors alike. This paper delves into the analysis of a Bitcoin price and volume dataset, spanning from September 2014 to July 2023. The objective is to extract multiple features related to price volatility and employ them to forecast the Bitcoin price for the subsequent 7 days. To achieve this, an Ensemble Learning Method (ELM) is proposed, able to estimate prices in both bullish and bearish markets. For price prediction, we consider three categories of predictors: 1) Bitcoin historical data; 2) volatility indicators; 3) trend prediction (price up or down) obtained through binary classification. Further, we employ a combination of ensemble models (regressors and classifiers) to predict the price at the daily level. The predictions of these models are stacked and weighted by the proposed ELM to improve the forecast accuracy. The ELM …

Simona Vasilica Oprea

Simona Vasilica Oprea

Academia de Studii Economice din Bucuresti

Electronics

PV-OPTIM: A Software Architecture and Functionalities for Prosumers

The future development of the energy sector is influenced by Renewable Energy Sources (RES) and their integration. The main hindrance with RES is that their output is highly volatile and less predictable. However, the utility of the RES can be further enhanced by prediction, optimization, and control algorithms. The scope of this paper is to disseminate a smart Adaptive Optimization and Control (AOC) software for prosumers, namely PV-OPTIM, that is developed to maximize the consumption from local Photovoltaic (PV) systems and, if the solar energy is not available, to minimize the cost by finding the best operational time slots. Furthermore, PV-OPTIM aims to increase the Self-Sustainable Ratio (SSR). If storage is available, PV-OPTIM is designed to protect the battery lifetime. AOC software consists of three algorithms: (i) PV Forecast algorithm (PVFA), (ii) Day Ahead Optimization Algorithm (DAOA), and (iii) Real Time Control Algorithm (RTCA). Both software architecture and functionalities, including interactions, are depicted to promote and replicate its usage. The economic impact is related to cost reduction and energy independence reflected by the SSR. The electricity costs are reduced after optimization and further significantly decrease in case of real-time control, the percentage depending on the flexibility of the appliances and the configuration parameters of the RTCA. By optimizing and controlling the load, prosumers increase their SSR to at least 70% in the case of small PV systems with less than 4 kW and to more than 85% in the case of PV systems over 5 kW. By promoting free software applications to enhance RES integration, we …

2023/12/29

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IEEE Access

Unveiling Weather-Induced Blackouts: A Ten-Year Analysis with Deep Learning-Driven Power Resilience Enhancement

When a rainy day affects the power grid, instead of enjoying the weather, many consumers face unplanned blackouts worldwide. Approximately 80% of blackouts in the US are weather-induced power outages. Through the amalgamation and meticulous preprocessing of diverse public datasets, encompassing variables such as maximum temperature, solar exposure, and precipitation levels, the study aims to unravel the intricate dynamics through which weather influences power resilience. We utilize over ten years of data from 47 local government areas. The analysis focuses on predicting future power outages using a state-of-the-art deep learning Long Short-Term Memory (LSTM) model. The results show a promising area under the Receiver Operating Characteristic (AUC ROC) curve of approximately 90% and a mean precision exceeding 96%. The experiments utilize a 5-fold cross-validation methodology to …

Megat Farez Azril Zuhairi

Megat Farez Azril Zuhairi

Universiti Kuala Lumpur

IEEE Access

Realtime Feature Engineering for Anomaly Detection in IoT based MQTT Networks

The MQTTset dataset has become a focal point in the realm of anomaly detection within IoT-based systems. This study involves into refining anomaly detection techniques by employing various filtering methods, such as data conversion, attribute filtering, handling missing values, and scaling. The primary objective is to enhance the identification of anomalies, with a particular focus on detecting Denial of Service (DoS) attacks. The research not only examines existing techniques but also addresses a significant gap in MQTT traffic anomaly detection. To fill this void, the study proposes the integration of the ’source’ attribute extracted from PCAP files, leveraging hand-crafted feature engineering. This addition aims to provide a more comprehensive understanding of the anomalies present in MQTT traffic. Moreover, the research undertakes the crucial task of categorizing and prioritizing anomalies based on their …

Megat Farez Azril Zuhairi

Megat Farez Azril Zuhairi

Universiti Kuala Lumpur

IEEE Access

Abusive Language Detection in Urdu Text: Leveraging Deep Learning and Attention Mechanism

The widespread use of the Internet and the tremendous growth of social media have enabled people to connect with each other worldwide. Individuals are free to express themselves online, sharing their photos, videos, and text messages globally. However, such freedom sometimes leads to misuse, as some individuals exploit this platform by posting hateful and abusive comments on forums. The proliferation of abusive language on social media negatively impacts individuals and groups, leading to emotional distress and affecting mental health. It is crucial to automatically detect and filter such abusive content in order to effectively tackle this challenging issue. Detecting abusive language in text messages is challenging due to intentional word concealment and contextual complexity. To counter abusive speech on social media, we need to explore the potential of machine learning (ML) and deep learning (DL …

Kishore Bingi

Kishore Bingi

VIT University

IEEE Access

Lower Output Voltage Harmonics with Optimum Switching Angles of Single PV-Source Based Reduced Switch Multilevel Inverter using BWO Algorithm

This paper presents a technique of harmonic minimization from output voltage waveform of a reduced switch Multilevel Inverter (MLI) through an efficient bio inspired metaheuristic algorithm called Black widow optimization (BWO). The proposed reduced switch 13- level MLI scheme uses a single Photovoltaic (PV) source which can be suitable for grid integration. The proposed BWO algorithm minimizes the Total Harmonic Distortion (THD) of output voltage with low operational time compared to other existing nature based algorithms considering large searching area. The weighted THD (WTHD) of the output voltage is also minimized in order to reduce the effect of lower order harmonics from the output voltage in a greater extent. The convergence rate and level of accuracy of BWO algorithm is compared with two different bio inspired algorithms for justification. The MLI operation is carried out with fundamental …

Giuseppe Acri

Giuseppe Acri

Università degli Studi di Messina

IEEE Access

Assessment of exposure to spatially varying magnetic fields in MRI environments: modelling analysis for simulation tools

Magnetic resonance imaging (MRI) is a non-invasive diagnostic technique widely used in medicine with more than 60 million exams per year performed worldwide. MRI personnel are always exposed to static and spatially heterogeneous magnetic fields (fringe or stray fields) and motion-induced time-varying magnetic fields during the working day. This kind of exposure can evoke vertigo and other sensory perceptions such as nausea, visual sensations, and a metallic taste which are not considered hazardous per se, but can be disturbing and may impair working ability. Up to now, no standardized procedures have been available in the literature for the assessment of occupational exposure in an MRI environment. The goal of this paper is to give some indications about the analytical models underlying the development of digital tools for occupational exposure assessment in MRI environments, to have easy but …

Ali Rostami

Ali Rostami

University of Tabriz

IEEE Access

All-Optical Broadband QDs Semiconductor Optical Amplifier (QDs-SOA): Inhomogeneous Broadening

The escalating demand for increased traffic capacity and bandwidth in communication networks has spurred the exploration of innovative solutions. This article delves into the promising features of Quantum Dot Semiconductor Optical Amplifiers (QD-SOAs), specifically focusing on InAs0.4Sb0.6 quantum dots within an InP quantum well structure. The study utilizes optical pumping to achieve population inversion in the active region, thereby enhancing efficiency and overcoming challenges associated with electrical pumping. Through numerical simulations employing the Finite Difference Time Domain (FDTD) technique, the article successfully demonstrates the amplification of a 6μm wavelength optical signal, achieving a gain of 7.8 times (8.92 dB). Furthermore, the impact of inhomogeneous and homogeneous broadenings on the amplifier’s structure and gain is thoroughly explored, exposing their crucial roles in …

Simon X. Yang

Simon X. Yang

University of Guelph

IEEE Access

The Optimal Global Path Planning of Mobile Robot Based on Improved Hybrid Adaptive Genetic Algorithm in Different Tasks and Complex Road Environments

In complex environments, mobile robots performing tasks with different hazard levels need to consider different road factors, this paper proposes a functional model correlating task hazard levels with road factors, proposing an innovative Hybrid Adaptive Genetic Algorithm (HAGA). The HAGA integrates an optimized two-optimization (2-opt) operator* with an enhanced Adaptive Genetic Algorithm (AGA) for efficient path planning in diverse tasks and complex road conditions. Firstly, pre-optimize the initial paths is performed by introducing a new domain knowledge-based operator that duplicates paths in the path are deleted to avoid the redundant paths, and then they are divided into the TOP layer and the ordinary layer, the TOP layer is optimized by using the adaptive 2-opt* operator that satisfies the hyperbolic tangent function (TANH), and the crossover and variability of the ordinary layer are optimized by using the …

Luca Vollero

Luca Vollero

Università Campus Bio-Medico di Roma

IEEE Access

A benchmarking on Optofluidic microplastic pattern recognition: A systematic comparison between statistical detection models and ML-based algorithms

Microplastics, small particles of plastic found in the environment, have become an increasingly worrying topic in recent years. This paper compares a statistical detection model to classifiers from various supervised learning paradigms in order to detect microplastics. The objective of this paper is to present a benchmark for detecting microplastics using statistical and machine learning models. The main goal is to assess and compare their performance when the defined parameters deviate from the optimal solution of the respective model. Results are presented in terms of probability error, comparing the performance of the machine learning techniques to the statistical model. The study considers a range of signal-to-noise ratios and a priori event probabilities, focusing on the classifiers’ ability to handle amplitude variability and threshold variation. Results show that as the number of analyzed particles in the flow …

Panagiotis Trakadas

Panagiotis Trakadas

National and Kapodistrian University of Athens

IEEE Access

Federated Learning-Aided Prognostics in the Shipping 4.0: Principles, Workflow, and Use Cases

The next generation of shipping industry, namely Shipping 4.0 will integrate advanced automation and digitization technologies towards revolutionizing the maritime industry. As conventional maintenance practices are often inefficient, costly, and unable to cope with unexpected failures, leading to operational disruptions and safety risks, the need for efficient predictive maintenance (PdM), relying on machine learning (ML) algorithms is of paramount importance. Still, the exchange of training data might raise privacy concerns of the involved stakeholders. Towards this end, federated learning (FL), a decentralized ML approach, enables collaborative model training across multiple distributed edge devices, such as on-board sensors and unmanned vessels and vehicles. In this work, we explore the integration of FL into PdM to support Shipping 4.0 applications, by using real datasets from the maritime sector. More …

Panagiotis Trakadas

Panagiotis Trakadas

National and Kapodistrian University of Athens

IEEE Access

Improving Connectivity in 6G Maritime Communication Networks with UAV Swarms

The deployment of maritime communication networks (MCNs) enables Internet-of-Things (IoT) applications, related to autonomous navigation, offshore facilities and smart ports. Still, the majority of maritime nodes, residing in MCNs lacks reliable connectivity. Towards this end, integrating unmanned aerial vehicles (UAVs) in sixth generation (6G) MCN topologies results in the formation of an aerial segment, complementing shore base stations that may offer insufficient coverage, and satellite communication, characterized by increased delays. In this study, we focus on an MCN where the direct links towards a shore BS are not available, due to excessive fading conditions. For this case, we use a UAV swarm to provide improved wireless connectivity, adopting non-orthogonal multiple access (NOMA) for high resource efficiency. In downlink communication, UAVs take into consideration the desired service rate and the …

Panagiotis Trakadas

Panagiotis Trakadas

National and Kapodistrian University of Athens

IEEE access

Leveraging Network Data Analytics Function and Machine Learning for Data Collection, Resource Optimization, Security and Privacy in 6G Networks

The full deployment of sixth-generation (6G) networks is inextricably connected with a holistic network redesign able to deal with various emerging challenges, such as integration of heterogeneous technologies and devices, as well as support of latency and bandwidth demanding applications. In such a complex environment, resource optimization, and security and privacy enhancement can be quite demanding, due to the vast and diverse data generation endpoints and associated hardware elements. Therefore, efficient data collection mechanisms are needed that can be deployed at any network infrastructure. In this context, the network data analytics function (NWDAF) has already been defined in the fifth-generation (5G) architecture from Release 15 of 3GPP, that can perform data collection from various network functions (NFs). When combined with advanced machine learning (ML) techniques, a full-scale …