Aid Of Artificial Neural Networks Research Articles

Exploring the structure of the digital economy through blockchain technology and mitigating adverse environmental effects with the aid of artificial neural networks

The rapid expansion of the digital economy has had a transformative impact on society, presenting both opportunities and challenges. This article aims to examine the structure of the digital economy and its implications, with a specific focus on the adverse environmental effects associated with its rapid growth. To address these challenges, the utilization of artificial neural networks is proposed as a viable solution. ANNs have proven to be effective in analyzing large volumes of data and extracting valuable insights. By integrating blockchain technology and harnessing the power of ANNs, this study seeks to develop management strategies that optimize resource allocation, reduce waste, and promote sustainability within the digital economy. Through comprehensive data analysis, patterns and trends can be identified, providing decision-makers with valuable information to make informed choices that minimize the environmental impact of digitalization. This research significantly contributes to the existing body of knowledge by enhancing our understanding of the digital economy’s structure, particularly in the context of blockchain technology. The ANN in this study estimated the impact of digital economy growth and structure improvement on adverse environmental effects, waste reduction, and environmental sustainability. The predictions showed that increasing digital economy growth led to increased waste reduction and promotion of environmental sustainability, while adverse environmental effects exhibited sinusoidal behavior. Linear regression confirmed the acceptable error of the network’s predictions compared to experimental results. Furthermore, it sheds light on the potential of ANNs to mitigate the adverse environmental effects associated with the digital economy. By emphasizing the importance of sustainable practices and exploring the applications of emerging technologies, this study offers valuable insights for policymakers, researchers, and industry practitioners seeking to navigate the complex landscape of the digital economy while minimizing its environmental consequences.

Network Modeling Approach for the Nonlinear Dynamics of the Osseous Bio-Physics and Bio-Mathematics

Bone is termed the smart material, and its modeling is of great interest in biomechanics, biochemistry, endocrinology, and oncology. The bone health of cancer patients is directly affected by hormonal imbalances, receptor-mediated tumor-targeted processes, and disturbed bone mineral density. Researchers have used different therapeutic approaches to monitor bone health during and after cancer treatment. This paper describes a reverse process of bone rebuilding after the resorption of bone via cancer treatment. A detailed model is used for hormonal therapy, which leads to the physical changes in trabecular structure. These changes are demonstrated with the aid of artificial neural networks and Petri nets. This paper connects the structural modeling of the bone trabecula with chemical kinetics. The main goal of this study is to provide a PN model of bone metastasis and an analysis of its structural properties. These properties are very helpful in demonstrating the complex dynamics of bone metastasis. Although both ANNs and PNs are well organized in the areas of machine learning and network modeling, neither technique is without limitations. ANNs, for example, are very efficient machine-learning applications, but their utter lack of explanation capabilities classifies them as a “black-box” technique. On the other hand, PNs are an effective modeling technique, but their theory does not include machine learning. This paper provides a hybrid approach to address the two approaches in a novel manner.

Design and development of solar photovoltaic fed modular multilevel inverter using intelligent techniques for renewable energy applications

This paper presents the design and development of Modular Multilevel Inverter (MMI) to reduce Total harmonic distortion (THD) using intelligent techniques towards marine applications. Many researchers have described the additional advantage of inverter control challenges such as voltage imbalance, increasing the number of voltage levels, power quality issues, reducing the number of semiconductors switches and achieving higher efficiency. Under the intelligent techniques, the implementation is carried out with aid of Artificial Neural Networks (ANN), Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to calculate the modulation index (ma) and switching angles (θ) for MMI. Based on the calculation, it is trained to form a mapping between inputs and outputs for obtaining reduced Total Harmonics Distortion (THD). The objective of the intelligent controller is to control the inverter for regulating the output voltage with lowest THD. The proposed control structure has been estimated and compared for better robustness in terms of switching angle and modulation index with least THD in the inverter. Simulations and prototype models are made to analyze the controller’s performance, for inverter output voltage and harmonics. This proposed system is designed for marine lighting load application. The FPGA performance with all intelligent methods are analyzed by in SPARTAN3E500 FPGA device.

Multivariate anomaly detection based on prediction intervals constructed using deep learning

It has been shown that deep learning models can under certain circumstances outperform traditional statistical methods at forecasting. Furthermore, various techniques have been developed for quantifying the forecast uncertainty (prediction intervals). In this paper, we utilize prediction intervals constructed with the aid of artificial neural networks to detect anomalies in the multivariate setting. Challenges with existing deep learning-based anomaly detection approaches include (i) large sets of parameters that may be computationally intensive to tune, (ii) returning too many false positives rendering the techniques impractical for use, and (iii) requiring labeled datasets for training which are often not prevalent in real life. Our approach overcomes these challenges. We benchmark our approach against the oft-preferred well-established statistical models. We focus on three deep learning architectures, namely cascaded neural networks, reservoir computing, and long short-term memory recurrent neural networks. Our finding is deep learning outperforms (or at the very least is competitive to) the latter.

Post-COVID-19 Neural Cryptographic Onset of Homeopathy Psychiatry Medicine's Transmission in "New Normal Form"

In telepsychiatric treatments, the medical data of various types of patients' data are to be communicated in a well-protected way between the patient and the psychiatrists. Homeopathy drugs play a significant role in treating such psychiatric patients. The objective of this manuscript is to develop a strong session key with the help of neural cryptography. Neural cryptography is the extensive usage of the artificial neural networks for the cryptographic functions. E-medicines were encrypted here by the classical methods and the proposed session keys of 128 bits long. The length has been selected to 128 bits in order to have less memory space needed. Alzheimer's disease, Dementia, Insomnia, Stress, OCD, Brain restlessness, Depression, etc are the most clinical mental complications observed in this post COVID-19 situations. This is the era of digital health to deal the remote patients who are non-invasive in nature. In this manuscript, a session key has been proposed by the Key Distribution Centre (KDC) with the aid of artificial neural networks. Strong session keys were derived of 128 bits length which in turn used for AES 128 encryption. To avoid the Man-InThe-Middle attacks, it has been designed that the server machine shall accumulate all the needful symptoms of the psychiatric patients.

A Sustainable Facility Management Outsourcing Relationships System: Artificial Neural Networks

The Contingency Outsourcing Relationship (CORE) model originated from the Four Outsourcing Relationship Types (FORT) model; the CORE model is used in the globalized Facility Management (FM) industry, while the FORT model is originally used in the global information technology industry. The purpose of this paper is to thoroughly analyse the simulated case studies of the four different categories (i.e., in-house, technical expertise, commitment and common goals) of the CORE model from the perspective of the various clients. This study builds on the previous work on the outsourcing relationships between a client and a globalized FM service provider. It further explores the application of this model with the aid of artificial neural networks (ANNs) towards a sustainable future. A quantitative methodology through a survey is used to analyse eight outsourcing strategies for the four outsourcing relationships. A set of revised rules of the CORE is introduced and discussed regarding the approaches to investigate the four simulated outsourcing relationship systems. The study further reveals that an interesting understanding of the four outsourcing categories can be systematically and efficiently implemented into the FM outsourcing relationships through the methodology of scientific Artificial Intelligence (AI). It is concluded that FM outsourcing categorization may help to define the appropriate relationships. This further detailed outcome generated from the ANN can be clearly considered a strong and solid reference to define and explain the existing outsourcing relationships between the stakeholders and the service providers with the aim to assign an outsourcing category to the FM relationship between the client and service provider based on the learnt rules.

Optimization of Tartrazine Adsorption onto Polypyrrole/SrFe12O19/Graphene Oxide Nanocomposite Using Central Composite Design and Bat Inspired Algorithm with the Aid of Artificial Neural Networks

This project was aimed to focus on the application of bat inspired algorithm with the aid of artificial neural networks (ANN-BA) as a novel metaheuristic algorithm in chemistry and environmental sciences for optimization of tartrazine dye adsorption onto the polypyrrole/SrFe12O19/graphene oxide (PPy/SrM/GO) nanocomposite from aqueous solutions. The PPy/SrM/GO nanocomposite was fabricated by an in situ polymerization process and its structural and magnetic properties were studied by means of several instrumental techniques. Four factors affecting adsorption process were optimized in a batch system by ANN-BA and central composite design (CCD). In comparison to the CCD, the ANN- BA model obtained through levenberg marquardt back propagation methodology, gave higher percentage removal (94 %) about 6 %. Under optimal conditions obtained by ANN-BA, the values of four factors including initial concentration, adsorbent dosage, pH, and shaking rate were 15 mg/l, 0.02 g, 6.5, and 297 rpm, respectively. In the above conditions, the experimental results were fitted well to the pseudo-second-order kinetic model with the rate constant (k2) of 0.038 g/mg/min and the Langmuir adsorption isotherm with monolayer maximum capacity (qm) of 123.5 mg/g with determination coefficients (R2) of 0.9986 and 0.9989, respectively. Thermodynamic studies revealed that tartrazine adsorption was spontaneous in all temperatures (ΔG 89), magnetic separable and reusable adsorbent (R%>50 after the sixth regeneration cycle) in environmental cleanup.

Globalized service providers’ perspective for facility management outsourcing relationships

PurposeThe Contingency Outsourcing Relationship (CORE) model originated from the Four Outsourcing Relationship Types (FORT) model and the CORE model is used in the globalized facility management (FM) industry while the FORT model is originally used in the global information technology industry. The purpose of this paper is to analyze the CORE model through the rankings of relationship between a client and a globalized FM service provider from the perspective of the FM service provider in one of the four categories (i.e. in-house, technical expertise, commitment and common goals) and the application of this model with the aid of artificial neural networks (ANNs).Design/methodology/approachA quantitative methodology using a survey is used to analyze the four types of outsourcing categories. First, the background theory and a set of rules of the CORE is introduced and discussed regarding the proper ways to identify the rankings collected from the survey.FindingsThe study reveals that an interesting understanding of the outsourcing categories can be systematically implemented into the FM outsourcing relationships through the methodology of scientific artificial intelligence. FM outsourcing categorization may help to define the appropriate relationship; as either not too aggressive or too passive.Originality/valueThe outcome generated from the ANN can be considered a strong and solid reference to assess and define the existing outsourcing relationships between the stakeholders and the service providers with the goal to assign an outsourcing category to the service provider based on the learnt rules.

Rapid extraction of copper ions in water, tea, milk and apple juice by solvent-terminated dispersive liquid–liquid microextraction using p-sulfonatocalix (4) arene: optimization by artificial neural networks coupled bat inspired algorithm and response surface methodology

A bat inspired algorithm with the aid of artificial neural networks (ANN-BA) has been used for the first time in chemistry and food sciences to optimize solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a green, fast and low cost technique for determination of Cu2+ ions in water and food samples using p-sulfonatocalix (4) arene as a complexing reagent. For this purpose, the influence of four important factors four factors which was influenced on the extraction efficiency such as salt addition, solution pH and disperser and extraction solvent volumes were investigated. Central composite design (CCD) as a comparative technique was employed for optimization of ST-DLLME efficiency. The ANN-BA optimization technique was regarded as a superior model due to its higher value of extraction efficiency (about 7.21%) compared to CCD method. Under ANN-BA optimal conditions, the limit of quantitation (S/N = 10), limit of detection (S/N = 3) and linear range were 0.35, 0.12 and 0.35-1000µgL-1, respectively. In these circumstances, the percentage recoveries for drinking tea, apple juice, milk, bottled drinking water, river and well water spiked with 0.05, 0.1 and 0.2mg L-1 of Cu2+ ions were in the acceptable range (91.4-107.1%). In comparison to other methods, the developed ST-DLLME method showed the lowest solvent and sample consumption, shortest value of extraction time, most suitable determination and detection limits and linear range with simple and low cost apparatus. Additionally, the use of bat inspired algorithm as a powerful metaheuristic algorithm with the aid of artificial networks is another advantage of the present work.

Simultaneous extraction of Cu2+ and Cd2+ ions in water, wastewater, and food samples using solvent-terminated dispersive liquid-liquid microextraction: optimization by multiobjective evolutionary algorithm based on decomposition.

Solvent-terminated dispersive liquid-liquid microextraction (ST-DLLME) as a simple, fast, and low-cost technique was developed for simultaneous extraction of Cd2+ and Cu2+ ions in aqueous solutions. Multiobjective evolutionary algorithm based on decomposition with the aid of artificial neural networks (ANN-MOEA/D) was used for the first time in chemistry, environment, and food sciences to optimize several independent variables affecting the extraction efficiency, including disperser volume and extraction solvent volume, pH, and salt addition. To perform the ST-DLLME operations, xylene, methanol, and dithizone were utilized as an extraction solvent, disperser solvent, and chelating agent, respectively. Non-dominated sorting genetic algorithm versions II and III (NSGA II and NSGA III) as multiobjective metaheuristic algorithms and in addition central composite design (CCD) were studied as comparable optimization methods. A comparison of results from these techniques revealed that ANN-MOEA/D model was the best optimization technique owing to its highest efficiency (97.6% for Cd2+ and 98.3% for Cu2+). Under optimal conditions obtained by ANN-MOEAD, the detection limit (S/N = 3), the quantitation limit(S/N = 10), and the linear range for Cu2+ were 0.05, 0.15, and 0.15-1000μgL-1, respectively, and for Cd2+ were 0.07, 0.21, and 0.21-750μgL-1, respectively. The real sample recoveries at a spiking level of 0.05, 0.1, and 0.3mgL-1 of Cu2+ and Cd2+ ions under the optimal conditions obtained by ANN-MOEA/D ranged from 94.8 to 105%.

Estimation of echocardiogram parameters with the aid of impedance cardiography and artificial neural networks.

The advent of cardiovascular diseases as a disease of mass catastrophy, in recent years is alarming. It is expected to spread as an epidemic by 2030. Present methods of determining the health of one's heart include doppler based echocardiogram, MDCT (Multi Detector Computed Tomography), among various other invasive and non-invasive hemodynamic monitoring techniques. These methods require expert supervision and costly clinical set-ups, and cannot be employed by a common individual to perform a self diagnosis of one's cardiac health, unassisted. In this work, the authors propose a novel methodology using impedance cardiography (ICG), for the determination of a person's cardio-vascular health. The recorded ICG signal helps in extraction of features which are used for estimating parameters for cardiac health monitoring. The proposed methodology with the aid of artificial neural network is able to determine Stroke Volume (SV), Left Ventricular End Systolic Volume (LVESV), Left Ventricular End Diastolic Volume (LVEDV), Left Ventricular Ejection Fraction (LVEF), Iso Volumetric Contraction Time (IVCT), Iso Volumetric Relaxation Time (IVRT), Left Ventricular Ejection Time (LVET), Total Systolic Time (TST), Total Diastolic Time (TDT), and Myocardial Performance Index (MPI), with error margins of ±8.9%, ±3.8%, ±1.4%, ±7.8%, ±16.0%, ±9.0%, ±9.7%, ±6.9%, ±6.2%, and ±0.9%, respectively. The proposed methodology could be used in screening of precursors to cardiac ailments, and to keep a check on the cardio-vascular health.

Bees metaheuristic algorithm with the aid of artificial neural networks for optimization of acid red 27 dye adsorption onto novel polypyrrole/SrFe12O19/graphene oxide nanocomposite

In this work, the capability of bees-inspired algorithm with the aid of artificial neural networks (ANN-BA) for optimization of acid red 27 dye removal was studied using polypyrrole/SrFe12O19/graphene oxide (GO) as a novel nanocomposite. The nanocomposite fabricated through in situ polymerization and its characteristics were investigated by means of several instrumental techniques. The prepared polypyrrole/SrFe12O19/GO nanocomposite was employed to adsorb acid red 27 from aqueous solution. Based on the cost function as a nonlinear relation between some factors influencing the adsorption efficiency including adsorbent dosage, initial concentration, pH, shaking rate and contact time with percentage removal which were obtained by a multilayer perceptron artificial neural networks, bees metaheuristic algorithm was utilized to optimize of the batch sorption process. In addition, D-optimal response surface methodology (RSM) was also employed as a comparative study. In comparison with the D-optimal RSM, the ANN-BA model gave higher percentage removal (99%) about 4%. Under optimal conditions obtained by ANN-BA, equilibrium isotherms, kinetic behaviors and thermodynamics of the dye adsorption were thoroughly investigated. The pseudo-second-order model and the Langmuir adsorption model [with maximum capacity (qmax) of 294.11 mg g−1] fitted the experimental results with the determination coefficients (R2) of 0.94 and 0.99, respectively. The thermodynamic parameters have also been evaluated which showed the sorption procedure was endothermic and spontaneous. The findings obtained from the sorbent usage in wastewater treatment and its regeneration investigation revealed that the nanocomposite can be applied as an effective dye sorbent for removing acid red 27 dye in real samples with a reusable property.

Development of a New Aprepitant Liquisolid Formulation with the Aid of Artificial Neural Networks and Genetic Programming.

In the present study, liquisolid formulations were developed for improving dissolution profile of aprepitant (APT) in a solid dosage form. Experimental studies were complemented with artificial neural networks and genetic programming. Specifically, the type and concentration of liquid vehicle was evaluated through saturation-solubility studies, while the effect of the amount of viscosity increasing agent (HPMC), the type of wetting (Soluplus® vs. PVP) and solubilizing (Poloxamer®407 vs. Kolliphor®ELP) agents, and the ratio of solid coating (microcrystalline cellulose) to carrier (colloidal silicon dioxide) were evaluated based on in vitro drug release studies. The optimum liquisolid formulation exhibited improved dissolution characteristics compared to the marketed product Emend®. X-ray diffraction (XRD), scanning electron microscopy (SEM) and a novel method combining particle size analysis by dynamic light scattering (DLS) and HPLC, revealed that the increase in dissolution rate of APT in the optimum liquisolid formulation was due to the formation of stable APT nanocrystals. Differential scanning calorimetry (DSC) and attenuated total reflection FTIR spectroscopy (ATR-FTIR) revealed the presence of intermolecular interactions between APT and liquisolid formulation excipients. Multilinear regression analysis (MLR), artificial neural networks (ANNs), and genetic programming (GP) were used to correlate several formulation variables with dissolution profile parameters (Y 15min and Y 30min) using a full factorial experimental design. Results showed increased correlation efficacy for ANNs and GP (RMSE of 0.151 and 0.273, respectively) compared to MLR (RMSE=0.413).

Effect of Varying the Substrates and Turns of a SR in the Design of Edge Chamfered Microstrip Patch Antenna through Artificial Neural Networks and Meta Resonators

In this paper, the design of size reduced patch antenna for various wireless applications using Artificial Neural Networks (ANN) is presented. Two Network; Cascade forward Back propagation and Feed forward Back Propagation Networks are Simulated, Trained and Tested to yield the optimized design of the Conventional Patch. The conventional patch is designed to resonate at 2.785 GHz with the aid of ANN and an FEM based simulator; while the proposed rectangular patch has triple resonances 1.78, 2.58, 2.87 GHz. The antenna is conveniently modified in to a Heptagon by chamfering one of its edges. Spiral resonator is effectively used in its ground plane to create miniaturization. The Antenna has a gain of 2.368, 2.706, 4.262 dBs respectively. Keywords: Rectangular microstrip patch antenna (RMPA), Slots, Miniaturisation, Linear Polarization, WLAN, WIMAX, DCS, ANN, FF, CF.

Determination of the mechanical and physical properties of cartilage by coupling poroelastic-based finite element models of indentation with artificial neural networks

One of the most widely used techniques to determine the mechanical properties of cartilage is based on indentation tests and interpretation of the obtained force–time or displacement-time data. In the current computational approaches, one needs to simulate the indentation test with finite element models and use an optimization algorithm to estimate the mechanical properties of cartilage. The modeling procedure is cumbersome, and the simulations need to be repeated for every new experiment. For the first time, we propose a method for fast and accurate estimation of the mechanical and physical properties of cartilage as a poroelastic material with the aid of artificial neural networks. In our study, we used finite element models to simulate the indentation for poroelastic materials with wide combinations of mechanical and physical properties. The obtained force–time curves are then divided into three parts: the first two parts of the data is used for training and validation of an artificial neural network, while the third part is used for testing the trained network. The trained neural network receives the force–time curves as the input and provides the properties of cartilage as the output. We observed that the trained network could accurately predict the properties of cartilage within the range of properties for which it was trained. The mechanical and physical properties of cartilage could therefore be estimated very fast, since no additional finite element modeling is required once the neural network is trained. The robustness of the trained artificial neural network in determining the properties of cartilage based on noisy force–time data was assessed by introducing noise to the simulated force–time data. We found that the training procedure could be optimized so as to maximize the robustness of the neural network against noisy force–time data.

Condition-Based Maintenance for medium speed diesel engines used in vessels in operation

Condition-Based Maintenance for diesel engines has contributed to reliability, energy-efficiency, and cost reduction. Both, the modelling of engine performance and fault detection require large amounts of data; usually, these are obtained on a test bench. In contrast, in operative engines, provoking faults onboard is not a viable proposition. Condition-Based Maintenance, fault detection and diagnosis need to be solved on engines installed in commercial vessels: the present contribution answers this need. A medium-speed diesel engine was monitored using thermocouples, pressure sensors, a propeller shaft torque meter and fuel oil flow-meters, during more than 10,000 running hours. Monitored data were used to train a three-layer feed-forward neural network, to generate the engine performance model; thus, determine the engine's fuel consumption and faulty conditions. The faulty conditions considered were: (1) a polluted turbine; (2) a dirty air filter/compressor; (3) a dirty air cooler; (4) and bad fuel injection, i.e. bad combustion. The sensor's precision and the experience gained by monitoring the engine served as a baseline to define the fault threshold values. The results proved the feasibility of installing a Condition-Based Maintenance, for vessels in operation, by monitoring engine performance and analysing the data with the aid of artificial neural networks.

Spectrophotometric simultaneous determination of metal ions in cows' milk and vegetables with the aid of artificial neural networks using synthetic 2‐benzylspiro[isoindoline‐1,5′‐oxazolidine]‐2′,3,4′‐trione

A simple, selective and sensitive multi-component method for the simultaneous determination of Zn(2+) , Mn(2+) and Fe(3+) based on complex formation with 2-benzylspiro[isoindoline-1,5'-oxazolidine]-2',3,4'-trione using artificial neural networks is proposed. The analytical data showed that metal-to-ligand ratios in Zn(2+) and Fe(3+) complexes was 1:1 and for Mn(2+) complex was 1:2. It was found at pH 6.5 and 5 min after mixing, the complexation reactions were completed. The coloured complexes exhibited absorption bands in the wavelength range 200-400 nm. The results showed that Zn(2+) , Mn(2+) and Fe(3+) could be determined in the range 0.1-18.0, 0.3-10.0 and 0.5-20.0 mg L(-1) , respectively. The data obtained from synthetic mixtures of metal ions were processed by radial basis function networks (RBFNs) and back-propagation neural network. The optimal conditions of the neural networks were obtained by adjusting various parameters. Satisfactory precision and accuracy were obtained with all networks, although, because of surprisingly lower root mean square error (%) values, RBFNs were the preferred approach. The proposed approach was tested by analysing the composition of the different mixtures containing Zn(2+) , Mn(2+) and Fe(3+) . The proposed method was successfully applied to the simultaneous determination of Zn(2+) , Mn(2+) and Fe(3+) ions in milk and vegetable samples.

Simultaneous determination of Mn2+ and Fe3+ as 4,4′[(4-cholorophenyl)methylene] bis(3-methyl-1-phenyl-1H-pyrazol-5-ol) complexes in some foods, vegetable and water samples by artificial neural networks

A simple and sensitive spectrophotometric method to the simultaneous determination of Mn2+ and Fe3+ in foods, vegetable and water sample with the aid of artificial neural networks (ANNs) is described. It relies on the complexation of analytes with recently synthesised bis pyrazol base ligand as 4,4′[(4-cholorophenyl)methylene] bis(3-methyl-1-phenyl-1H-pyrazol-5-ol)(CMBPP). The analytical data show that the ratio of ligand to metal in metal complexes is 1:1 and 1:2 for Fe3+ and Mn2+, respectively. It was found that the complexation reactions are completed at pH 6.7 and 5min after mixing. The results showed that Mn2+ and Fe3+ could be determined simultaneously in the range of 0.20–7.5 and 0.30–9.0mgl−1, respectively. The analytical characteristics of the method such as the detection limit and the relative standard error predictions were calculated. The data obtained from synthetic mixtures of the metal ions were processed by radial basis function networks (RBFNs) and feed forward neural networks (FFNNs). The optimal conditions of the neural networks were obtained by adjusting various parameters by trial-and-error. Under the working conditions, the proposed methods were successfully applied to the simultaneous determination of elements in different water, tablet, rice, tea leaves, tomato, cabbage and lettuce samples.

A New Approach in Financial Modelling with the Aid of Artificial Neural Networks

In recent years there has been significant interest in Artificial Neural Networks (ANNs) and their suitability for the solution of many real-world problems. When simulating complex systems which display highly nonlinear behaviour, traditional methods prove insufficient either due to their oversimplified problem description or due to the lack of available computer resources. ANNs have been shown to be quite successful when applied to such problems whose dynamics may not be explicitly understood. The use of ANNs has become more widespread with increasing computing processing power. In this work we present a brief overview of the ANN model developed and its validation and concentrate on specific applications in the area of financial modelling, where the network manages to reconstruct with quite high accuracy the behaviour of a very complex problem, such as the prediction of the performance of a bank stock. This constitutes the first step towards the creation of a framework which enables mathematical models to benefit from the ability of ANNs to resolve complex trends.

WITHDRAWN: Modeling of thermal cracking reactor with the aid of artificial neural networks

WITHDRAWN: Modeling of thermal cracking reactor with the aid of artificial neural networks