Role Of Artificial Neural Networks Research Articles

Quality Evaluation Methods of College English Teaching Reform Based on Artificial Neural Network

This paper aims to enhance the effectiveness of intelligent evaluation in college English teaching and explore the role of artificial neural networks in evaluating college English teaching reform. A research study is conducted to develop an intelligent evaluation model by refining algorithm models and utilizing comparative research methods to address limitations of traditional algorithms. The neural network algorithms are improved through model construction techniques, with practical effectiveness assessed through simulation training methods. The algorithm model is applied to assess teaching quality in universities, with experimental research integrating questionnaire surveys and simulation data processing methods. The feasibility of the approach is verified through data evaluation. Simulation analysis demonstrates that the enhanced neural network algorithm reduces running time and enhances accuracy compared to traditional evaluation algorithms. Experimental studies indicate that employing artificial neural networks for evaluating the quality of college English teaching reform yields positive outcomes, aiding students and teachers in understanding their situations within mobile computing environments. This study confirms that combining a neural network model with data survey methods effectively meets the requirements of contemporary efficient teaching quality evaluation. The innovative integration of intelligent algorithms, factor analysis, surveys, and statistical methods enhances the progressive nature of teaching quality evaluation.

The Role of Artificial Neural Networks (ANNs) in Supporting Strategic Management Decisions

The Role of Artificial Neural Networks (ANNs) in Supporting Strategic Management Decisions

Enhancing Urban Sustainability: The Role of Artificial Neural Networks in Energy Efficiency and Smart City Development

Enhancing Urban Sustainability: The Role of Artificial Neural Networks in Energy Efficiency and Smart City Development

A new cyber-security approach for load frequency control of hybrid interconnected renewable power systems

Countries worldwide are embracing the trend toward smart grids, depending on advanced technology and communication networks. However, the smart grid is more vulnerable to cyberattacks than its traditional counterpart, especially the false data injection attack (FDIA), which aims to add false data into the transmitted signals to cause undesirable behavior. This article proposes a new scheme to detect and estimate the FDIA on load frequency control (LFC) of modern interconnected power systems considering renewable energy sources (RESs) and high voltage direct current (HVDC) link. The proposed scheme is based on the Kalman filter (KF) and artificial neural networks (ANNs). The KF is used to detect the FDIA in the transmitted system signals and indicates the possibility of an attack in each transmitted signal, where the role of ANNs is to estimate the transmitted signal before attacking. Hence, based on KF and ANNs, the proposed scheme can ensure the detection of false data and remove the attack simultaneously. The proposed cyber-security scheme is considered a thorough solution that can effectively reduce the impact of the FDIA on any of the four transmitted signals within the two-area interconnected power system, all while simulating the virtual inertia control strategy. The simulation results of the studied low-inertia interconnected power system are carried out using MATLAB/Simulink® software. The simulation results successfully demonstrate the proposed scheme's efficacy, accuracy, and acceptability to detect and eliminate the FDIA on the LFC of low-inertia interconnected power grids.

The role of artificial neural networks in prediction of severe acute pancreatitis associated acute respiratory distress syndrome: A retrospective study.

Early identification and intervention of acute respiratory distress syndrome (ARDS) are particularly important. This study aimed to construct predictive models for ARDS following severe acute pancreatitis (SAP) by artificial neural networks and logistic regression. The artificial neural networks model was constructed using clinical data from 214 SAP patients. The patient cohort was randomly divided into a training set and a test set, with 149 patients allocated to the training set and 65 patients assigned to the test set. The artificial neural networks and logistic regression models were trained by the training set, and then the performance of both models was evaluated using the test set. The sensitivity, specificity, PPV, NPV, accuracy, and AUC value of artificial neural networks model were 68.0%, 87.5%, 77.3%, 81.4%, 80.0%, 0.853 ± 0.054 (95% CI: 0.749-0.958). The sensitivity, specificity, PPV, NPV, accuracy and AUC value of logistic regression model were 48.7%, 85.3%, 65.5%, 74.4%, 72.0%, 0.799 ± 0.045 (95% CI: 0.710-0.888). There were no significant differences between the artificial neural networks and logistic regression models in predictive performance. Bedside Index of Severity in Acute Pancreatitis score, procalcitonin, prothrombin time, and serum calcium were the most important predictive variables in the artificial neural networks model. The discrimination abilities of logistic regression and artificial neural networks models in predicting SAP-related ARDS were similar. It is advisable to choose the model according to the specific research purpose.

Dry Powder Inhaler with the technical and practical obstacles, and forthcoming platform strategies.

A Dry Powder Inhaler (DPI) is a technique as well as a device used to inhale formulation which is in the form of dry powder, and is inhaled through the nose or mouth. It was developed for the purpose of treating conditions like chronic obstructive pulmonary disease (COPD), Asthma, and even cystic fibrosis etc. The aim of the review is to discuss the different methods of preparation of dry powders along with the characterization of DPI. Here we present the outline of different methods like supercritical fluid extraction (SCF), spray drying, and milling. The review focussed on various devices including single and multi-dose devices used in the DPI. It also highlights on recent advances in the DPI including nano particulate system, siRNA-based medication, liposomes, and pro-liposomes based delivery. In COVID-19 silver nanoparticles-based DPIs provide very prominent results in the infected lungs. Moreover, this review states that the AI-based DPI development provides and improvement in the bioavailability and effectiveness of the drug along with the role of artificial neural networks (ANN). The study also showed that nasally administered drugs (nose to brain) can easily cross the blood-brain barrier (BBB) and enter the central nervous system (CNS) through the olfactory and trigeminal pathway which provides effective CNS concentrations at lower dosage. It is suggested that DPIs not only target respiratory complications but also treat CNS complications too. This review provides support and guides the researcher in the recent development and evaluation of DPI.

The role of artificial neural network and machine learning in utilizing spatial information

In this age of the fourth industrial revolution 4.0, the digital world has a plethora of data, including the internet of things, mobile, cybersecurity, social media, forecasts, health data, and so on. The expertise of machine learning and artificial intelligence (AI) is required to soundly evaluate the data and develop related smart and automated applications, These fields use a variety of machine learning techniques including supervised, unsupervised, and reinforcement learning. The objective of the study is to present the role of artificial neural networks and machine learning in utilizing spatial information. Machine learning and AI play an increasingly important role in disaster risk reduction from hazard mapping and forecasting severe occurrences to real-time event detection, situational awareness, and decision assistance. Some of the applications employed in the study to analyze the various ANN domains included weather forecasting, medical diagnosis, aerospace, facial recognition, stock market, social media, signature verification, forensics, robotics, electronics hardware, defense, and seismic data gathering. Machine learning determines the many prediction models for problems involving classification, regression, and clustering using known variables and locations from the training dataset, spatial data that is based on tabular data creates different observations that are geographically related to one another for unknown factors and places. The study presents that the Recurrent neural network and convolutional neural network are the best method in spatial information processing, healthcare, and weather forecasting with greater than 90% accuracy.

A state of the art on characterization and application of artificial neural networks on bond strength between steel rebar and concrete

• This review article helps to summarize the interaction between the steel rebars and concrete. • The various equations and models developed by many researchers and codes are discussed. • The ANN predicts the interaction between rebar and concrete in a relatively short time with low error rates. • The introduction of ANN model can save time, reduce the material wastage thereby reduce the construction cost. The reinforcement of concrete with steel results in stress transfer from steel to concrete and hence it becomes essential to understand the bond and anchorage behaviour of concrete. A strong bond between the steel reinforcement and concrete is a major contributing factor to the durability of reinforced concrete structures. This article focusses on reviewing the bond strength of concrete with steel rebars considering various parameters such as compressive strength, diameter of the bar, concrete cover, bar geometry and anchorage length using pull-out and beam bond tests. The role of artificial neural networks (ANN) in predicting the bond strength of concrete using these parameters is also discussed. Various international codes and literature discussing the pull-out and beam bond tests have provided a better understanding of the bond properties of concrete.

Application of Artificial Neural Networks in Chemical Process Control

An important data-driven model is the artificial neural network. Artificial neural networks have been widely used in many domains of chemical processes due to its robustness, fault tolerance, self-adaptive capability, and self-learning ability. For the chemical process with nonlinearity and strong coupling, artificial neural networks can model and control the process well and make up for the lack of traditional PID control technology. As a result, ANN has emerged as a significant positive trend for chemical process control. In this paper, the principle, development history, and common structure of artificial neural networks are first outlined. Then the role of artificial neural networks in chemical process control is introduced in three aspects: improved PID control, improved model predictive control, and for hybrid models. The important effect of artificial neural networks in chemical process control is reflected by comparison. Finally, it is proposed that chemical process control can be more developed by applying more deep learning algorithms and developing multiple neural networks and hybrid models in chemical process control.

Meta-heuristic Techniques to Train Artificial Neural Networks for Medical Image Classification: A Review

: Medical imaging has been utilized in various forms in clinical applications for better diagnosis and treatment of diseases. These imaging technologies help in recognizing body's ailing region easily. In addition, it causes no pain to the patient as the interior part of the body can be examined without difficulty. Nowadays, various image processing techniques such as segmentation, registration, classification, restoration, contrast enhancement and many more exist to enhance image quality. Among all these techniques, classification plays an important role in computer-aided diagnosis for easy analysis and interpretation of these images. Image classification not only classifies diseases with high accuracy but also analyses which part of the body is infected. The usage of Neural networks classifier in medical imaging applications has opened new doors or opportunities to researchers stirring them to excel in this domain. Moreover, accuracy in clinical practices and the development of more sophisticated equipment are necessary in the medical field for more accurate and quicker decisions. Therefore, keeping this in mind, researchers started using meta-heuristic techniques to classify the methods. This paper provides a brief survey on the role of artificial neural networks in medical image classification, various types of meta-heuristic algorithms applied for optimization purposes, and their hybridization. A comparative analysis showing the effect of applying these algorithms on some classification parameters such as accuracy, sensitivity, and specificity is also provided. From the comparison, it can be observed that the usage of these methods significantly optimizes these parameters leading us to diagnose and treat a number of diseases in their early stage.

Role of artificial neural networks in predicting design and efficiency of dye sensitized solar cells

Photovoltaic technology attracts researchers from industry and academia due to its potential in producing electricity directly from the sunlight. Among all the photovoltaic devices, the dye-sensitized solar cell has gained preference due to its low-cost fabrication and versatility in electrolytes, dye, substrate, and catalyst. The optical, electrical, and structural properties of the materials determine the power conversion efficiency of a solar cell. But, conducting experiments in the laboratory to identify the suitable materials for the fabrication of an efficient solar cell requires much time, cost, and human effort. The proven potential of machine learning techniques in pattern matching and computer vision motivated the researchers to employ these techniques for predicting the efficiency of solar cells. The research works conducted so far show the applications of these techniques in predicting the optimum efficiency, best suitable design, and material for the fabrication of Dye-Sensitized Solar Cells (DSSCs). In this paper, the authors present a comprehensive review of the machine learning techniques employed and the types of input data used for predicting the design and efficiency of solar cells. They also give essential insights into the selection of optimum parameters for selecting the materials for fabricating a substrate, dye sensitizer, semiconductor, electrolyte, and catalyst for designing the most efficient dye-sensitized solar cell without conducting experiments in the laboratory. This paper may prove a time and cost-saving assistant for developing a customized neural network model for predicting the efficiency of a DSSC from the dataset available in the literature. Highlights Artificial Neural Network (ANN) model is useful to identify the suitable materials for efficient DSSC assembly. The tailored neural network models minimize the need for hit and trial experiments. The hybrid of ANN and Genetic Algorithm (GA) offers a low-cost technological solution for DSSC assembly. The experimental data are vital for extracting useful insights for DSSC fabrication.

Role of Artificial Neural Networks in Pharmaceutical Sciences

<p style="text-align: justify;">Artificial Neural Networks (ANN) are becoming the tool of choice for the pharmaceutical industry due to their ability to mimic the brain&rsquo;s way of functioning. Computational and statistical methods have recently sparked the pharmaceutical industries interest in identifying possible pharmaceutical products that fulfil all technical requirements. Researchers are developing ANN from various scientific areas to overcome prediction, optimization, recognition, and control problems. Conventional techniques can only be used in specific, well-constrained situations. ANN analyses incomplete or unstructured information and converts it into more sensible analysable data by detecting the underlying patterns and similarities. This technology also creates new ideas by rearranging existing knowledge. For example, ANN can construct a promising modelling technique when data sets exhibit nonlinear correlations, which is prominent in pharmaceutical operational processes. In the pharmaceutical industry, this thinking network can be used in disease diagnosis, genomics and proteomics, drug design, to determine physicochemical properties of a drug, drug testing, optimization, pharmacokinetics, in vitro and in vivo correlations, and also to study drug interactions. In this short review article, various applications of ANN in pharmaceutical research are presented. <p style="text-align: justify;"><strong>Key words: </strong>Artificial Neural Networks, Pharmaceutical Sciences, Drug Design, Drug Discovery, Drug Delivery, Formulation Development.

The Role of Artificial Neural Networks in Prediction of Mechanical and Tribological Properties of Composites—A Comprehensive Review

The Role of Artificial Neural Networks in Prediction of Mechanical and Tribological Properties of Composites—A Comprehensive Review

A comparison of artificial neural network and classical regression models for earthquake-induced slope displacements

Empirical predictive models of earthquake-induced slope displacements are commonly developed through classical regression techniques that predefine a functional form and derive the model parameters using a large dataset of computed displacements. Machine learning (ML) techniques represent an attractive alternative to classical regression techniques because they can more easily capture complex interactions between the input and output variables without stipulating a specific functional form. This study develops predictive models for seismic slope displacement using the artificial neural network (ANN) approach and a database of slope displacements from finite element analyses, and compares the resulting ANN model with a classical regression model derived from the same dataset. The ANN utilizes the yield acceleration for the slope (ky), the natural period of the slope (Tslope), the depth of the slip surface relative to the height of the slope (Hratio), and the peak ground velocity (PGV) of the input motion to predict the slope displacement. Relative to the classical regression model generated from the same dataset, the ANN model modestly improves the median displacement at large and small PGV values and reduces the total variability in the displacement prediction by about 5%. The ANN model predicts displacements that vary more smoothly with the input parameters than the functional form used in the classical model. Although the ANN model only moderately improves predictions relative to the classical regression model, as functional forms for classical regression models become more complex, the role of ANN in providing a simpler regression alternative will grow.

Artificial Neural Network algorithm for image Compression and Edge Detection

In recent years, detection of things measured in the PASCAL VOC dataset has stabilized. Low-level image&#x0D; features and high-level context. There are many ways to perform complex grouping systems that combine image&#x0D; features in this work. An algorithm has been proposed to reveal the edge of the image in the proposed way Fully&#x0D; Connected Network (FC) by Convolution Neural Network (CNN) in short time using CNN method to reduce the&#x0D; space occupied by the original information of the image. To highlight the role of Artificial Neural Network (ANN) in&#x0D; the field of images to detect the edges using modern techniques in many areas of science, engineering and medicine,&#x0D; where the images of the cancerous cells algorithm that was created in this paper are revealed showing the importance&#x0D; of Artificial Neural Network (ANN) and Fully Connected Network (FC) by Convolution Neural Network (CNN) to&#x0D; detect cancer cells and tumors.

A Multi-Criteria Recommendation Framework using Adaptive Linear Neuron

Recent developments in the field of recommender systems have led to a renewed interest in employing some of the sophisticated machine learning algorithms to combine multiple characteristics of items during the process of making recom-mendations. Considerable number of research papers have been published on multi-criteria recommendation techniques. Most of these studies have focused only on using some basic statistical methods or simply by extending the similarity computation of the traditional heuristic-based techniques to model the system. Researchers have not treated the uncertainty that exists about the relationship between multi-criteria modelling approaches and effectiveness of some of the complex and powerful machine learning techniques; in fact, no previous study has investigated the role of artificial neural networks to design and develop the system using aggregation function approach. This paper seeks to remedy these challenges by analysing the performance of multi-criteria recommender systems, modelled by integrating an adaptive linear neuron that was trained using delta rule, and asymmetric sin-gular value decomposition algorithms. The proposed model was implemented, trained and tested using a multi-criteria dataset for recommending movies to users based on action, story, direction, and visual effects of movies. Taken together, the empirical results of the study suggested that there is a strong association between artificial neural networks and the modelling approaches of multi-criteria recommendation technique.

Role of Artificial Neural Network in an Intelligent Educational System

Role of Artificial Neural Network in an Intelligent Educational System

Resilience actions to counteract the effects of climate change and health emergencies in cities: the role of artificial neural networks.

Both the World Health Organization (WHO) with its 2015 "Climate and Health Country Profile Project" and the Istituto Superiore di Sanità (ISS) with its 2018 "Health and Climate Change", agree on the emergency generated by the climate change and concerning health problems. The mitigation strategy suggested by the Intergovernmental Panel On Climate Change (IPCC) against greenhouse gas emissions and their effects on climate change, has not yet yielded the desired results. It is therefore necessary to focus on adaptation strategies, to immediately counter the effects of climate change (CC) on most vulnerable people and environments, by increasing their resilience through local interventions and targeted resilience actions. Coordinated resilience actions are necessary to combat the effects of CC especially in urban areas. Useful tools to manage and optimize resilience actions are artificial neural networks (ANN) in complex and dynamic domains as cities are. The case of ANN applied to a city is presented as an example to increase the climate resilience of health local systems. In the current state of knowledge, ANN prove to be the most advanced and global solution to coordinate and manage a set of resilience actions in urban areas.

Adaptive Artificial Neural Network-Based Models for Instantaneous Power Estimation Enhancement in Electric Vehicles’ Li-Ion Batteries

This paper investigates the role of artificial neural networks in enhancing the accuracy of instantaneous power estimation of electric vehicles’ batteries. In electric vehicles, a battery is used as a main or complementary bidirectional power source. To optimize the energy management of the vehicle, the power sourced or sinked by the battery must be estimated in real time under any condition. The power of the battery is a function of many variables including the current, the state of charge, the ambient temperature, and the state of health. This paper evaluates some existing equivalent circuit models for estimating the instantaneous power of electric vehicles' batteries and proposes new artificial neural network-based models to enhance the power estimation accuracy. The experimental data obtained by performing standardized electric vehicle tests using a 3.6-V/16.5-Ah lithium-ion battery cell and a 12.8-V/150-Ah lithium-ion battery pack are presented and used for models' evaluation.

Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate

Can advances in NLP help advance cognitive modeling? We examine the role of artificial neural networks, the current state of the art in many common NLP tasks, by returning to a classic case study. In 1986, Rumelhart and McClelland famously introduced a neural architecture that learned to transduce English verb stems to their past tense forms. Shortly thereafter in 1988, Pinker and Prince presented a comprehensive rebuttal of many of Rumelhart and McClelland’s claims. Much of the force of their attack centered on the empirical inadequacy of the Rumelhart and McClelland model. Today, however, that model is severely outmoded. We show that the Encoder-Decoder network architectures used in modern NLP systems obviate most of Pinker and Prince’s criticisms without requiring any simplification of the past tense mapping problem. We suggest that the empirical performance of modern networks warrants a reexamination of their utility in linguistic and cognitive modeling.