Application Of Neural Networks Research Articles

Development of a hydroponic device using potassium Ion-Selective electrode and neural network technology

The rapid development of modern agricultural biotechnology and information technology has become a key driver of modern agricultural growth. The demand for ion detection is prevalent in all aspects of agriculture. This study details the research on an ion sensor agricultural system using potassium (K) ion-selective electrodes (ISEs). The main emphasis is on the application of artificial neural networks (ANNs) for driving ISE development of accuracy. ISEs based on solid contact with metal oxides, specifically manganese dioxide, are studied. Moreover, the design details of an ion signal acquisition device using the NI-9205 acquisition card and LabVIEW are presented. Finally, based on experimental results of nutrient solution samples, ANNs show better performance than the multiple linear regression method. The mean relative error of K-ISE detection was maintained within 4%. This study thoroughly discusses ISE technology to promote its adoption in agriculture.

Application of Neural Networks for Defect Detection in Rotationally Symmetric Components

Application of Neural Networks for Defect Detection in Rotationally Symmetric Components

Review of Electrical Steels with their Properties and Recent Trends for Improvement

Objectives: This review study provides a thorough analysis of the magnetic behavior and characteristics of electrical steels, including established and novel materials (3%wt Silicon). Methods: We investigate how composition, microstructure, and processing methods affect the magnetic performance of these steels as well as the basic principles guiding their behavior. Thermomechanical processing can be observed for its impact on the material’s microstructure and magnetic properties. This review discusses testing over samples for conventional processes for different % of cold worked or strain hardening and annealing at higher temperatures i.e., 1200°C. For uncertainties regarding the impact of impurities or inclusions on their magnetic properties, the study was carried out to know the effect of cerium addition on the material’s magnetic properties of the material. Findings: It was observed that rolling temperature has significant effect over magnetic properties of the material. A notable improvement in the magnetic permeability of the material and its critical performance metric for soft magnetic materials is indicated by the increase in “Gamma max”. Similarly, the effects of compressive stresses, doping effect, grain size, annealing, and other manufacturing processes were studied, and observed their impact on magnetic properties. This review discusses the literature on future scope with the latest trends. This helps clarify the trade-offs associated with maximizing electrical steels for certain uses. Novelty: This study presents a novel approach towards electrical steel and its magnetic properties by incorporating different alloying elements and optimizing rolling & annealing parameters. A key innovation is the application of machine learning and neural network to predict and optimize material behavior, core loss and provide methods to improve magnetic properties of electrical steels. Significance: Electrical steels are crucial in transformers and electric motors, enhancing efficiency by minimizing energy losses and improving magnetic performance. This can be achieved by changing manufacturing parameters and operating conditions with the help of latest trends. Keywords: Electrical steel, Magnetic properties, Machine learning, Composition, Microstructure, Core loss

Application of Convolutional Neural Network (CNN) and different other techniques for the restoration of degraded folk artworks: a comparative performance analysis

Application of Convolutional Neural Network (CNN) and different other techniques for the restoration of degraded folk artworks: a comparative performance analysis

Application of novel deep neural network on prediction of compressive strength of fly ash based concrete

Application of novel deep neural network on prediction of compressive strength of fly ash based concrete

IWF-TextRank Keyword Extraction Algorithm Modelling

Keywords are used to provide a concise summary of the text, enabling the quick understanding of core information and assisting in filtering out irrelevant content. In this paper, an improved TextRank keyword extraction algorithm based on word vectors and multi-feature weighting (IWF-TextRank) is proposed to improve the accuracy of keyword extraction by comprehensively considering multiple features of words. The key innovation is demonstrated through the application of a backpropagation neural network, combined with sequential relationship analysis, to calculate the comprehensive weight of words. Additionally, word vectors trained using Word2Vec are utilised to enhance the model’s semantic understanding. Finally, the effectiveness of the algorithm is verified from various aspects using traffic accident causation data. The results show that this algorithm demonstrates a significant optimisation effect in keyword extraction. Compared with the traditional model, the IWF-TextRank algorithm shows significant improvement in accuracy (p-value), recall (R-value), and F-value.

A Comprehensive Review on Accident Detection Using Artificial Intelligence and Machine Learning

This paper reviews the role of Artificial Intelligence (AI) and Machine Learning (ML) in accident detection systems, focusing on the application of image processing techniques and Convolutional Neural Networks (CNNs) to enable detection. Through a comprehensive analysis of recent methodologies, challenges, limitations, and future research directions, this paper aims to provide a detailed understanding of how AI and ML are advancing the field of accident detection

Speech Emotion Recognition Using Feedforward Neural Network

Speech Emotion Recognition (SER) has gained increasing attention due to its application in fields such as human-computer interaction, healthcare, customer service automation, and affective computing. This review focuses on the application of Deep Neural Networks (DNNs), specifically Feedforward Neural Networks (FNNs), in detecting and classifying emotions from speech signals. Recent advancements in deep learning have significantly enhanced the ability of machines to interpret emotional cues from auditory data. This paper discusses the motivation, objectives, and scope of employing DNN FNN architectures for SER. We also address the technical feasibility and potential of this approach for real-time applications. By reviewing existing literature, this study aims to provide insights into the current progress, challenges, and future prospects of SER systems.

Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method

The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases.

Perancangan Model Jaringan Syaraf Tiruan untuk Memprediksi Penyakit Demam Berdarah Menggunakan Algoritma Hebb Rule

Dangeu dengue fever or what we often call dengue fever is a disease transmitted by the Aedes aegypti mosquito and caused by the dengue virus. This disease can potentially cause serious complications if it does not receive proper treatment. In this research, the author uses the application of artificial neural networks with the Hebb rule approach to predict the risk level of dengue fever. Predictions are made based on factors such as weather conditions, population density and historical case data that influence this disease. The Hebb rule is used in this research because of its ability to strengthen connections between neurons based on the input patterns they receive, so it is hoped that it can produce more accurate predictions. Test results show that this method has a fairly high level of accuracy in predicting the pattern of dengue fever cases in an area. This research indicates that the application of artificial neural networks with the Hebb rule can be an effective tool for related parties in taking preventive measures to minimize the number of dengue cases in the future.

Robust neural networks using stochastic resonance neurons.

Various successful applications of deep artificial neural networks are effectively facilitated by the possibility to increase the number of layers and neurons in the network at the expense of the growing computational complexity. Increasing computational complexity to improve performance makes hardware implementation more difficult and directly affects both power consumption and the accumulation of signal processing latency, which are critical issues in many applications. Power consumption can be potentially reduced using analog neural networks, the performance of which, however, is limited by noise aggregation. Following the idea of physics-inspired machine learning, we propose here a type of neural network using stochastic resonances as a dynamic nonlinear node and demonstrate the possibility of considerably reducing the number of neurons required for a given prediction accuracy. We also observe that the performance of such neural networks is more robust against the impact of noise in the training data compared to conventional networks.

Non-conventional feature-based LSTM model for prediction of bearing performance degradation

Abstract Mechanical bearings are critical in electrical machines, such as motors and generators. They provide support and facilitate the movement of rotating components in these machines. They are essential to the operation of electrical machinery cause they provide support, reduce friction, ensure proper alignment, and improve overall reliability and efficiency. The appropriate selection, installation, and maintenance of these bearings allow the longevity of such machines. The maintenance and the extent of usage determine the usefulness of the bearings. In predictive maintenance and condition monitoring, accurately predicting the level of bearing degradation holds significant importance for strategizing maintenance tasks, reducing downtime, and avoiding unforeseen breakdowns. This study centers on predicting the level of bearing degradation through the application of neural networks, focusing mainly on non-conventional features, rather than the usual features like kurtosis, and skewness. The dataset utilized is sourced from NASA and includes diverse time series signals that provide insights into the entire lifespan of the bearing. In this investigation, advanced machine learning techniques, long-short term memory (LSTM) in particular, are employed to harness the intricate patterns within the bearing dataset, aiming to improve the accuracy of the predictions.

Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model’s efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model’s robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model’s exceptional generalizability and potential for real-world applications in electric vehicle battery management.

Predicting resilient modulus for pavement design: a comprehensive review of artificial neural network applications

ABSTRACT The Resilient Modulus (M R) is an essential parameter describing the mechanical behaviour of pavement materials, but the tests to obtain it are time-consuming and costly. Therefore, machine learning, especially Artificial Neural Networks (ANN), has recently been an effective alternative for predicting M R. Although there has been an increase in articles on ANN for predicting M R, no systematic review has yet categorised the publications, algorithm structures, predictive parameters, and model accuracy. This review provides a comprehensive overview of studies using ANN to predict M R for pavement materials. It examines various ANN subtypes and model structures, addressing a gap in understanding these models and identifying prevalent predictive parameters. Twenty-five peer-reviewed articles published in English-language journals were identified using keywords related to neural networks and M R. For fine soils, all models reviewed use moisture content as a predictive parameter. In contrast, granular soils models typically include stress state and physical characteristics of the materials. For recycled or stabilised materials, there is significant variability in model inputs. This review underscores the potential of ANN in enhancing predictive models for M R in pavement engineering, pointing towards future research and application refinement in this area of study.

Readout Methods to Enhance the Performance of Luminescence Thermometers

Over the past three decades, luminescence thermometry has gained significant attention among researchers and practitioners. The method has progressed in terms of utilizing temperature-sensitive luminescent materials, obtaining temperature read-outs from luminescence, developing applications, and improving performance. This paper reviews and critically analyzes routes for improving luminescence thermometry performance, in particular the sensitivity, accuracy, and precision of the method. These include the use of highly temperature-sensitive probes, temperature read-outs from luminescence with improved sensitivity, multiparameter temperature-reading methods, the applications of principal component analysis and artificial neural networks, and sensor fusion.

Application of convolutional neural networks in image classification and applications of improved convolutional neural networks

Abstract. This paper reviews the application and improvement of convolutional neural networks (CNNs) in image classification. Firstly, a shallow CNN for interstitial lung disease image classification is presented. This model suppresses overfitting through a unique network architecture and optimisation algorithm. Next, the improved VGG16 architecture and MIDNet18 model are discussed and their superior performance in brain tumour image classification is demonstrated. Subsequently, a CNN-CapsNet model for cervical cancer image classification and its improvement are presented and the customised model is compared with the conventional VGG-16 CNN architecture in the paper. Next, the application of sparse convolutional kernels and hybrid sparse convolutional kernels (HDCs) in solving the problem of computational resource consumption is presented. Subsequently, methods for solving the problem of limited training data through transfer learning and network data augmentation techniques are discussed, as well as GAN-generated datasets for solving the overfitting problem. Finally, the effect of degraded images on the classification effectiveness of CNNs is explored. The results show that the improved CNN architecture and algorithms have significant effects in solving the problems of overfitting and computational resource consumption, and can significantly improve the accuracy and efficiency of image classification. And degraded images do adversely affect the accuracy of CNN for image classification.

Application of LSTM recurrent neural networks for bitcoin price prediction

The growing interest in cryptocurrencies such as Bitcoin highlights the need for effective predictive models in this volatile market. This study developed and trained a model based on the Long Short-Term Memory (LSTM) Recurrent Neural Network architecture to forecast Bitcoin values with a low error percentage. The results confirmed the effectiveness of the LSTM model in predicting Bitcoin prices, demonstrating its ability to handle the high volatility characteristics of this market. Hypotheses regarding the efficiency of shorter versus longer lookback periods and the influence of data volume on model performance were tested. The experiments showed that increasing the volume of data used in training significantly increases the accuracy of predictions, evidenced by the lower error rates (MAPE and RMSE) obtained with larger data volumes. However, a saturation point was observed, after which further increases in data volume did not result in significant improvements. Regarding the lookback period, the results indicated that 30-day periods presented with the best performance, with lower forecast errors. Very short or very long lookback periods tend to increase the error, which highlights the importance of proper window selection for this type of model. Statistical analysis confirmed the significant influence of data volume and lookback period on model performance, although the interaction between these factors did not show statistical significance.

Neural Networks for Audio Classification: Multi-scale CNN-LSTM Approach to Animal Sound Recognition

Abstract. This research explores the application of neural networks, specifically CNN-LSTM models, for classifying sound signals from dogs, frogs, and cats, selected from the ESC-50 dataset. The sound data was preprocessed using Mel-frequency cepstral coefficients (MFCCs) and augmented through time stretching, pitch shifting, and noise addition to enhance model generalization in varied acoustic environments. We compared two deep learning models: a traditional CNN-LSTM and an improved version with multi-scale feature extraction, allowing for capturing both short-term and long-term sound patterns. Our findings show that the multi-scale CNN-LSTM architecture outperforms the traditional CNN-LSTM, achieving a test accuracy of 86.11% compared to 80.56%. These results highlight the effectiveness of multi-scale feature extraction for handling complex audio signals. This research offers valuable insights into bioacoustics and has broader applications in areas such as environmental sound monitoring, wildlife preservation, and animal behavior analysis.

Application of graph neural network in computational heterogeneous catalysis.

Heterogeneous catalysis, as a key technology in modern chemical industries, plays a vital role in social progress and economic development. However, its complex reaction process poses challenges to theoretical research. Graph neural networks (GNNs) are gradually becoming a key tool in this field as they can intrinsically learn atomic representation and consider connection relationship, making them naturally applicable to atomic and molecular systems. This article introduces the basic principles, current network architectures, and datasets of GNNs and reviews the application of GNN in heterogeneous catalysis from accelerating the materials screening and exploring the potential energy surface. In the end, we summarize the main challenges and potential application prospects of GNNs in future research endeavors.

Physiological states and body postures can tell your flow experience——application of BP neural networks

Physiological states and body postures can tell your flow experience——application of BP neural networks