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Publications - 6374

Artificial neural network analysis of chemical reaction and radiation effects on MHD ternary nanofluid flow over an exponentially accelerated inclined plate

S. V.K. Varma B. T. Raju Hijaz Ahmad Waleed Mohammed Abdelfattah Charankumar Ganteda Hameed Miya

Publication Name: South African Journal of Chemical Engineering

Publication Date: 2026-07-01

Volume: 57

Issue: Unknown

Page Range: Unknown

Description:

This investigation explores the magnetohydrodynamic (MHD) free convective heat and mass transfer characteristics of a ternary nanofluid traversing an exponentially accelerated inclined plate within a porous medium. The theoretical framework integrates the complexities of internal heat generation/absorption and fluctuating wall temperatures. Analytical solutions were rigorously derived utilizing the Laplace transform technique, while a sophisticated Artificial Neural Network (ANN) was implemented to forecast and corroborate these mathematical outcomes. Heat Transfer (Nusselt Number) evaluated against the interplay of the Prandtl number, thermal radiation parameters, and temporal progression. Mass Transfer (Sherwood Number) analyzed as a function of magnetic permeability, the Schmidt number, and time. Thermal Enhancement findings indicate that an augmentation in the nanofluid volume fraction significantly bolsters thermal conductivity, thereby elevating the temperature profile. The proposed Levenberg-Marquardt Algorithm-based Backpropagation Artificial Neural Network (LMA BANN) demonstrated exceptional predictive fidelity. The model achieved a precision threshold exceeding 99.9% for the Nusselt number and near-perfect accuracy for the Sherwood number. These results are substantiated by negligible Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) values, coupled with correlation coefficients (R) nearing unity, signifying a robust alignment between the analytical and predicted datasets.

Open Access: Yes

DOI: 10.1016/j.sajce.2026.100912

The status of arable plant habitats in eastern europe

G. Pinke

Publication Name: Changing Status of Arable Habitats in Europe A Nature Conservation Review

Publication Date: 2021-01-29

Volume: Unknown

Issue: Unknown

Page Range: 75-87

Description:

Today large parts of Eastern Europe can be considered as strikingly species-poor "agrarian-deserts". Nevertheless, the region also retains relatively large areas of species-rich farmland. Changes in the weed flora in this region, with special regard to the disappearing weed species, is the subject of relatively small numbers of international scientific studies compared to the western part of Europe. The average weed species number per plot seems to have declined less in eastern than in western countries since the Second World War. However, by the turn of the Millennium the number of threatened weed species had increased considerably, which is apparent in the recently updated national weed red lists. Many studies indicate that lower farming intensity and diversified farming systems at higher altitudes provided better conditions for the occurrence of rare species and greater diversity than intensively farmed lowlands. Unfortunately, only a few traditionally managed small fields remain in extreme habitats, and they are continuously being abandoned. Regrettably, Eastern Europe mostly lacks any conservation initiatives which directly target the preservation of rare and threatened arable weeds, consequently further declines are anticipated.

Open Access: Yes

DOI: 10.1007/978-3-030-59875-4_6

Development, Service-Oriented Architecture, and Security of Blockchain Technology for Industry 4.0 IoT Application

Satyanand Singh Joanna Rosak-Szyrocka László Tamándl

Publication Name: Hightech and Innovation Journal

Publication Date: 2023-03-01

Volume: 4

Issue: 1

Page Range: 134-156

Description:

The Internet of Things (IoT) paradigm is laying the groundwork for a world in which many of our everyday devices will be connected and will interact with their surroundings to gather data and automate some operations. Among other things, such a concept necessitates seamless authentication, data privacy, security, attack resilience, simplicity of deployment, and self-maintenance. Blockchain, a technology created with the Bitcoin cryptocurrency, can provide such advantages. To create blockchain-based IoT (BIoT) applications, a full discussion of how to modify blockchain to meet the unique requirements of IoT is offered in this paper. The most important BIoT applications are detailed after a brief introduction to blockchain, with the goal of highlighting how blockchain can affect conventional cloud-based IoT applications. Then, several factors that have an impact on the design, development, and deployment of a BIoT application are covered, along with present obstacles and potential improvements. Lastly, a list of recommendations is provided to help future BIoT researchers and developers understand some of the problems that need to be solved before deploying the upcoming generation of BIoT applications.

Open Access: Yes

DOI: 10.28991/HIJ-2023-04-01-010

On wavelet based enhancing possibilities of fuzzy classification methods

Ferenc Lilik Brigita Sziova Levente Solecki Laszlo T. Koczy Szilvia Nagy

Publication Name: Advances in Intelligent Systems and Computing

Publication Date: 2020-01-01

Volume: 945

Issue: Unknown

Page Range: 56-73

Description:

If the antecedents of a fuzzy classification method are derived from pictures or measured data, it might have too many dimensions to handle. A classification scheme based on such data has to apply a careful selection or processing of the measured results: either a sampling, re-sampling is necessary or the usage of functions, transformations that reduce the long, high dimensional observed data vector or matrix into a single point or to a low number of points. Wavelet analysis can be useful in such cases in two ways. As the number of resulting points of the wavelet analysis is approximately half at each filters, a consecutive application of wavelet transform can compress the measurement data, thus reducing the dimensionality of the signal, i.e., the antecedent. An SHDSL telecommunication line evaluation is used to demonstrate this type of applicability, wavelets help in this case to overcome the problem of a one dimensional signal sampling. In the case of using statistical functions, like mean, variance, gradient, edge density, Shannon or Rényi entropies for the extraction of the information from a picture or a measured data set, and they don not produce enough information for performing the classification well enough, one or two consecutive steps of wavelet analysis and applying the same functions for the thus resulting data can extend the number of antecedents, and can distill such parameters that were invisible for these functions in the original data set. We give two examples, two fuzzy classification schemes to show the improvement caused by wavelet analysis: a measured surface of a combustion engine cylinder and a colonoscopy picture. In the case of the first example the wear degree is to be determine, in the case of the second one, the roundish polyp content of the picture. In the first case the applied statistical functions are Rényi entropy differences, the structural entropies, in the second case mean, standard deviation, Canny filtered edge density, gradients and the entropies. In all the examples stabilized KH rule interpolation was used to treat sparse rulebases. The preliminary version of this paper was presented at the 3rd Conference on Information Technology, Systems Research and Computational Physics, 2–5 July 2018, Cracow, Poland [1].

Open Access: Yes

DOI: 10.1007/978-3-030-18058-4_5

Optimizing Computer Engineering Problems Using CODAS Method with Bipolar Complex Fuzzy Soft Frank Aggregation Operators

Tahir Mahmood Abdul Jaleel Muhammad Iftikhar Dragan Pamucr

Publication Name: Contemporary Mathematics Singapore

Publication Date: 2025-01-01

Volume: 6

Issue: 4

Page Range: 5145-5171

Description:

In this article, we start by explaining why Bipolar Complex Fuzzy Soft Set (BCFSS) is better than existing approaches by highlighting its abilities to enhance Computer Engineering (CpE) by removing ambiguity more effectively than Fuzzy Set (FS), Fuzzy Soft Set (FSS), Complex Fuzzy Soft Set (CFSS), Bipolar Fuzzy Set (BFS), Bipolar Fuzzy Soft Set (BFSS), and Bipolar Complex Fuzzy Set (BCFS). This will improve effectiveness as well as clarity. Make sure that those new to fuzzy sets properly understand the key issue in CpE. Reveal first the BCFSS’s new strategy and then detail how it differs from existing approaches, pointing out the overlooked features of previous approaches that justified your research. Enhance the preparation concept link by explaining how BCFSS enhances CpE Decision-Making (DM) in situations when conventional models are inadequate when paired with Frank Aggregation Operator (AO) (Frank Arithmetic Aggregation Operator (FAAO) and Frank Geometric Aggregation Operator (FGAO)). Moreover, keeping in mind the effectiveness of the “Combinative Distance-based Assessment (CODAS) method” we developed this method in the framework of BCFSSs and used this method and develop AOs to solve some decision-making problems related to CpE. Our sentence structure should also be refined to provide smooth transitions between ideas and to enhance readability and navigation. Lastly, provide comparison studies that highlight the improvement’s superiority over current methods to show that it is feasible.

Open Access: Yes

DOI: 10.37256/cm.6420256727

Comparison of Data Visualization Platforms Through European Traffic Data

Eniko Nagy Gombos Tamas Ferenc Gyorgy Molnar

Publication Name: Iccc 2024 IEEE 11th International Conference on Computational Cybernetics and Cyber Medical Systems Proceedings

Publication Date: 2024-01-01

Volume: Unknown

Issue: Unknown

Page Range: 43-48

Description:

The most important and valuable resource in the 21st century is data. The age of the internet brought us an era in which humans, within a foreseeable time, are unable to process the vast amount of data that we produce. It's important to understand the data and statistics we have because it can help a company make a well thought out decision on a given dilemma, which is based on reality and data. When choosing a data visualisation platform, the purpose of the application, the type of data and the user needs are also key factors. For the visualisation of traffic data in Europe, a number of established platforms can be considered and are referred to in this paper. This article compares data visualisation platforms currently available and used in industrial environments with open source free platforms, using European traffic data.

Open Access: Yes

DOI: 10.1109/ICCC62278.2024.10582968

Stability of fuzzy cognitive maps with interval weights

Istvan A. Harmati Laszlo T. Koczy

Publication Name: Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology Eusflat 2019

Publication Date: 2020-01-01

Volume: Unknown

Issue: Unknown

Page Range: 756-763

Description:

In fuzzy cognitive maps (FCMs) based modelling paradigm, the complex system's behaviour is gathered by the causal connections acting between its main characteristics or subsystems. The system is represented by a weighted, directed digraph, where the nodes represent specific subsystems or features, while the weighted and directed edges express the direction and strength of causal relations between them. The state of the complex system represented by the so-called activation values of the nodes, that is computed by an iterative method. The FCM based decision-making relies on the assumption that this iteration reaches an equilibrium point (fixed point), but other types of behaviour, namely limit cycles and chaotic patterns may also show up. In practice, the weights of connections are estimated by human experts or machine learning methods. Both cases have their own uncertainty, which can be represented by using intervals as weights instead of crisp numbers. In this paper, sufficient conditions are provided for the existence and uniqueness of fixed points of fuzzy cognitive maps that are equipped with interval weights, which also ensure the global asymptotic stability of the system.

Open Access: Yes

DOI: DOI not available

Overall Equipment Effectiveness (OEE) Life Cycle at the Automotive Semi-Automatic Assembly Lines

János Jósvai Péter Dobra

Publication Name: Acta Polytechnica Hungarica

Publication Date: 2022-01-01

Volume: 19

Issue: 9

Page Range: 141-155

Description:

In the automotive industry, manufacturing companies are constantly improving and monitoring their processes with different Key Performance Indicators (KPIs) in order to achieve higher profits. One of the KPIs is the Overall Equipment Effectiveness (OEE), which represents the efficiency of the different machines and assembly lines. High OEE percentage means good performance and quality. Using Manufacturing Execution System (MES) data the OEE contributors such as availability, performance and quality are calculated and followed at the manufacturing area day by day. This paper concentrates on the entire OEE life cycle at the automotive semi-automatic assembly lines. Firstly, a literature review demonstrates scientific relevance. Secondly, the phases of OEE life cycle are revealed and presented regarding a passenger car seat structure production life cycle. Third section points at the connection between OEE percentage and maintenance, labour and quality costs at the assembly lines. In addition to the theoretical approach, real, practical data are also demonstrated based on experiences from the last fifteen years.

Open Access: Yes

DOI: 10.12700/aph.19.9.2022.9.8

The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach

Laszlo Vasa Ashutosh Kolte Jewel Kumar Roy

Publication Name: Resources Policy

Publication Date: 2023-01-01

Volume: 80

Issue: Unknown

Page Range: Unknown

Description:

Global stock markets are incredibly unpredictable. Resource prices have a significant market impact on varying securities. With the use of cutting-edge technology like artificial intelligence, analysts and researchers are employing various machine learning techniques and econometrics methodologies to anticipate stock price trends in order to better comprehend stock market volatility. Volatility is the degree of variation in a time sequence of market rates. Stock market equity returns depend on the business output where the investor has trust in high and low equity. This research explores the interaction between industrialized and developing economies' market volatility relationships between 2000 and 2020 as well as the aforementioned impacts taking place on developing financial prudence worldwide. The aim of the study is to integrate an appropriate GARCH framework to estimate the uncertainty dependent on market conditions in the European Union, the Pacific, South America, Latin America, East Asia, West Asia and South Asia stock return indices. The Generalized Auto-Regressive Conditional Heteroscedasticity method is used for analyzing the effect of updates from the USA that influences the returns of S&P 500 globally as well as European Union, Pacific, South American, Latin American, East Asian, West Asian and South Asian indices returns. For capital markets of the world, there is a significant gap in equity return uncertainty. Such results have major effects on investors looking to diversify their portfolios. For international and domestic institutional shareholders, this paper is significant. The impact of international institutional investors' investments and effects of the growth of the equity market return may be omitted as the analysis is restricted exclusively to the European Union, the Pacific, South America, Latin America, East Asia, West Asia, and South Asia.

Open Access: Yes

DOI: 10.1016/j.resourpol.2022.103216