Deep Neural Network Algorithm Research Articles

Performance Comparison between Deep Neural Network and Machine Learning based Classifiers for Huntington Disease Prediction from Human DNA Sequence.

Huntington Disease (HD) is a type of neurodegenerative disorder which causes problems like psychiatric disturbances, movement problem, weight loss and problem in sleep. It needs to be addressed in earlier stage of human life. Nowadays Deep Learning (DL) based system could help physicians provide second opinion in treating patient's disease. In this work, human Deoxyribo Nucleic Acid (DNA) sequence is analyzed using Deep Neural Network (DNN) algorithm to predict the HD disease. The main objective of this work is to identify whether the human DNA is affected by HD or not. Human DNA sequences are collected from National Center for Biotechnology Information (NCBI) and synthetic human DNA data are also constructed for process. Then numerical conversion of human DNA sequence data is done by Chaos Game Representation (CGR) method. After that, numerical values of DNA data are used for feature extraction. Mean, median, standard deviation, entropy, contrast, correlation, energy and homogeneity are extracted. Additionally, the following features such as counts of adenine, thymine, guanine and cytosine are extracted from the DNA sequence data itself. The extracted features are used as input to the DNN classifier and other machine learning based classifiers such as NN (Neural Network), Support Vector Machine (SVM), Random Forest (RF) and Classification Tree with Forward Pruning (CTWFP). Six performance measures are used such as Accuracy, Sensitivity, Specificity, Precision, F1 score and Mathew Correlation Co-efficient (MCC). The study concludes DNN, NN, SVM, RF achieve 100% accuracy and CTWFP achieves accuracy of 87%.

Performance Evaluation of Solar PV Integrated with Custom Power Device under Various Load Conditions

The non-linear properties and rapid switching of power electronic equipment are the primary causes of power quality issues in power systems, particularly in the power distribution systems. The widespread use of delicate equipment, which continuously pollutes the environment, is making power quality problems worse. The increasing integration of renewable energy sources into the generation mix and the decarburization of the economy have created new challenges for smart grid technology, requiring creative solutions such as energy storage systems and smart transformers. This article describes a solar photovoltaic integrated unified power quality conditioner (UPQC) that uses a deep learning method based on neural networks and a novel compensating technique. Here, two Deep Neural Network algorithms are used, one in the solar PV system to obtain the maximum power under various irradiance situations, and the other to manage the UPQC under various load conditions. When compared to the conventional UPQC based on PQ Theory, DNN-UPQC produces superior results in terms of reducing total harmonic distortion. This iterative strategy, which is focused on soft computing, provides faster convergence to the target condition while maintaining the updating weight within a predetermined limit. PV-based UPQC has been connected individually to reduce voltage sag, swell, and unbalance in variable load conditions. The system's dynamic and steady state performance are assessed by modelling it with a MATLAB-Simulink in different load condition.

Improvement for deep learning for suicide and depression identification with unsupervised label correction

Abstract. Our study try to address the challenge of accurately identifying depression and suicidal ideation on social media platforms by introducing an enhanced novel methodology for unsupervised feature selection and label correction. Utilizing advanced word embedding models like BERT and the Universal Sentence Encoder, we transform textual content into dense numerical vectors that capture the nuanced emotional context of online discussions. Our approach enhances these embeddings with a deep neural network (DNN) to extract distinctive features, reducing the dimensionality of the data through Principal Component Analysis (PCA). For label correction, we employ clustering techniques including OPTICS, K-medoids, and hierarchical clustering, which are robust against noisy data points. We then train classifiers using CNN, DNN, logistic regression, and random forest algorithms, evaluated with metrics such as accuracy, precision, recall, F1 score, and AUC. This methodology improves the accuracy of classifying depressive and suicidal sentiments to some extent, assisting to utilize the vast data available on social media to advance mental health diagnostics and interventions.

Bearing Life Prediction Model for Electromechanical Equipment by Integrating Deep Neural Network and K-Nearest Neighbor Algorithm and Its Application

Bearing Life Prediction Model for Electromechanical Equipment by Integrating Deep Neural Network and K-Nearest Neighbor Algorithm and Its Application

Safety evaluation for the dismantling of long-span spatial lattice structures based on deep learning and graph traversal

The demolition issue of long-span spatial lattice structures has become increasingly prominent with the rapid urban development and building renewal. Compared with traditional demolition techniques, building deconstruction offers superior economic and social benefits. Dismantling in blocks is one of the primary ways to realize the deconstruction of long-span spatial lattice structures, yet the safety is rarely studied. To fill this gap, a safety evaluation framework was established based on deep learning and graph traversal algorithm in this study. Hazardous configuration of the remaining structure is the basis of dismantling safety evaluation. To collect the hazardous configurations, an identification method in conjunction with deep neural network (DNN) and depth-first search (DFS) algorithm was proposed. The dismantling safety is measured by the improved structural well-formedness whose allowable range is determined by statistical analysis of hazardous configurations. The applicability of the framework was illustrated by the safety evaluation for the dismantling of single-layer reticulated domes. Results indicated that the dismantling safety is significantly affected by the configuration quality of the remaining structure, and the DNN-based prediction model is effective to classify the configuration state. Additionally, a large number of hazardous configurations were obtained through the identification method, and the allowable value of the safety evaluation index was determined to be 0.7. The configuration quality mainly depends on the geometry and boundary conditions. This novel framework provides scientific and effective methodological guidance for engineers, promoting the further application of building deconstruction.

Real-Time Resolution Enhancement of Confocal Laser Scanning Microscopy via Deep Learning

Confocal laser scanning microscopy is one of the most widely used tools for high-resolution imaging of biological cells. However, the imaging resolution of conventional confocal technology is limited by diffraction, and more complex optical principles and expensive optical-mechanical structures are usually required to improve the resolution. This study proposed a deep residual neural network algorithm that can effectively improve the imaging resolution of the confocal microscopy in real time. The reliability and real-time performance of the algorithm were verified through imaging experiments on different biological structures, and an imaging resolution of less than 120 nm was achieved in a more cost-effective manner. This study contributes to the real-time improvement of the imaging resolution of confocal microscopy and expands the application scenarios of confocal microscopy in biological imaging.

Winter wheat identification in China through FY-3D NDVI&EVI satellite data and DNN Fusion

ABSTRACT Medium Resolution Spectral Imager-II (MERSI-II) on FengYun 3D (FY-3D) meteorological satellite is a visible and infrared spectral imaging instrument comparable with Moderate Resolution Imaging Spectroradiometer (MODIS), which provides ample opportunities for regional crop mapping. However, current research may have overlooked the potential of combining FY-3D MERSI-II data with deep neural network (DNN) algorithms for winter wheat identification. This research utilizes a DNN method to train a nonlinear model with the synthetic normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at a 250 m resolution from FY-3D MERSI-II as the eigenvalues, enabling to accurately identify spatial distribution of winter wheat in China’s main production regions and filling the gap in the application of FY-3D MERSI-II data. The results indicate that the winter wheat identification with the combination of NDVI and EVI achieved an accuracy of 95.32%, with a Kappa coefficient of 0.87 and a determination coefficient (R 2 ) of 0.73, improving the overall accuracy by 1.27% and 1.07% compared to models trained solely using NDVI or EVI, respectively. In terms of spatial distribution, the overlap rate with the winter wheat dataset of China from MODIS at a 500 m resolution exceeded 95%, with an area bias of 2.67%. This study indicates that the medium resolution FY-3D MERSI-II remote sensing dataset combined with the DNN algorithm can accurately identify crop information over large areas.

Elemental diffusion coefficient prediction in conventional alloys using machine learning

This paper presents the Machine Learned Diffusion Coefficient Estimator, a comprehensive machine learning framework designed to predict diffusion coefficients in impure metallic (IM) and multi-component alloy (MCA) media. The framework incorporates five machine learning models, each tailored to specific diffusion modes: (1) impurity and (2) self-diffusion in IM media, and (3) self, (4) impurity, and (5) chemical diffusion in MCA media. These models use statistical aggregations of atomic descriptors for both the diffusing elements and the diffusion media, along with the temperature of the diffusion process, as features. Models are trained using the random forest and deep neural network algorithms, with performance evaluated through the coefficient of determination (R2), mean squared error (MSE), and uncertainty estimates. The models within this framework achieve an impressive R2 score above 0.90 with MSE less than 10−16 m2/s, demonstrating high predictive accuracy and reliability for diffusion coefficient.

Determination of heat transfer characteristics of tube bundles over heating plate by machine learning

In this study machine learning is used with the aid of a deep neural network algorithm to predict convective heat transfer characteristics for inline and staggered tube bundles, and correlation equations for Nusselt number and friction factor are derived. Machine learning algorithm's data were obtained from experimental work for various transverse pitch of the tube bundles, longitudinal pitch of the tube bundles and Reynolds number. 276 experimental data points were taken for both inline and staggered tube bundles. However, considering that the data obtained from the experimental study may be insufficient for training, a two-step data augmentation method and retraining with cross-validation was used to prevent data deficiency in the deep neural network structure. Thus, the unseen data in the experimental work were also predicted. The coefficient of determination for the DNN model predictions was obtained greater than 0.96. One correlation equation for Nusselt Number and three correlation equations for friction factor were proposed from the augmented data with machine learning. The R2 values of the correlation equations varied between 89 % and 99 %. As a result, machine learning methods successfully applied to predict the Nusselt number and friction factor of tube bundles consistent with the experimental data.

Development of machine learning-based quantitative structure–activity relationship models for predicting plasma half-lives of drugs in six common food animal species

Abstract Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure–activity relationship (QSAR) model incorporating multiple machine learning and artificial intelligence algorithms, and aims to predict the plasma half-lives of drugs in 6 food animals, including cattle, chickens, goats, sheep, swine, and turkeys. By integrating 4 machine learning algorithms with 5 molecular descriptor types, 20 QSAR models were developed using data from the Food Animal Residue Avoidance Databank (FARAD) Comparative Pharmacokinetic Database. The deep neural network (DNN) algorithm demonstrated the best prediction ability of plasma half-lives. The DNN model with all descriptors achieved superior performance with a high coefficient of determination (R2) of 0.82 ± 0.19 in 5-fold cross-validation on the training sets and an R2 of 0.67 on the independent test set, indicating accurate predictions and good generalizability. The final model was converted to a user-friendly web dashboard to facilitate its wide application by the scientific community. This machine learning-based QSAR model serves as a valuable tool for predicting drug plasma half-lives and extralabel withdrawal intervals in 6 common food animals based on physicochemical properties. It also provides a foundation to develop more advanced models to predict the tissue half-life of drugs in food animals.

Machine Learning Approaches for Automated Diagnosis of Cardiovascular Diseases: A Review of Electrocardiogram Data Applications.

Cardiovascular diseases (CVDs) have been identified as the leading cause of mortality worldwide. Electrocardiogram (ECG) is a fundamental diagnostic tool used for the diagnosis and detection of these diseases. The new technological tools can help enhance the effectiveness of ECGs. Machine learning (ML) is widely acknowledged as a highly effective approach in the realm of computer-aided diagnostics. This article presents a review of the effectiveness of ML algorithms and deep-learning algorithms in diagnosing, identifying, and classifying CVDs using ECG data. The review identified relevant studies published in the 5 major databases: PubMed, Web of Science (WoS), Scopus, Springer, and IEEE Xplore; between 2021 and 2023, a total of 30 were chosen for the comprehensive quantitative and qualitative. The study demonstrated that different datasets are available online with data related to CVDs. The various ML techniques are employed for the purpose of classification. Based on our investigation, it has been observed that deep learning-based neural network algorithms, such as convolutional neural networks and deep neural networks, have demonstrated superior performance in the detection of entire record data. Furthermore, deep learning showcases its efficacy even when confronted with a scarcity of data. ML approaches utilizing ECG data exhibit a notable proficiency in the realm of diagnosis, hence holding the potential to mitigate the occurrence of disease-related consequences at advanced stages.

Monitoring the effects of climate, land cover and land use changes on multi-hazards in the Gianh River watershed, Vietnam

Abstract In recent decades, global rapid urbanization has exacerbated the impacts of natural hazards due to changes in Southeast Asia’s environmental, hydrological, and socio-economic conditions. Confounding non-stationary processes of climate change and global warming and their negative impacts can make hazards more complex and severe, particularly in Vietnam. Such complexity necessitates a study that can synthesize multi-dimensional natural-human factors in disaster risk assessments. This synthesis study aims to assess and monitor climate change and land-cover/land-use change impacts on flood and landslide hazards in Vietnam’s Gianh River basin. Three Deep Neural Network (DNN) and optimization algorithms, including the Adam, Tunicate Swarm Algorithm (TSA), and Dwarf Mongoose Optimization (DMOA) were used to determine the regions with the probability of the occurrence of flood and landslide and their combination. All efficiently evaluated hazard susceptibility based on a synthesis analysis encompassing 14 natural and anthropogenic conditioning factors. Of the three, the Deep Neural Network (DNN)-DMOA model performed the best for both flood and landslide susceptibility, with area-under-curve values of 0.99 and 0.97, respectively, followed by DNN-TSA (0.97 for flood, 0.92 for landslide), and DNN-Adam (0.96 for flood, 0.89 for landslide). Although the area affected by flooding is predicted to decrease, the overall trend for total hazard-prone areas increases over 2005–2050 due to the more extensive area affected by landslides. This study develop and demonstrate a robust framework to monitor multi-hazard susceptibility, taking into account the changes in climate and land-use influence the occurrence of multiple hazards. Based on the quantitative assessment, these findings can help policymakers understand and identify confounding hazard issues to develop proactive land-management approaches in effective mitigation or adaptation strategies that are spatially and temporally appropriate.

Predicting sessile droplet evaporation kinetics via cascaded deep networks and tree-based machine learning approach

The intricate nature of sessile droplet evaporation phenomenon makes detailed experimental studies time consuming and requires sophisticated apparatus; while complete numerical simulations are computationally expensive and also time-intensive. In this article, for the first time, we explore the applicability of machine learning (ML) approaches to predict the evaporation kinetics of generalized sessile droplets under various conditions. An in-house dataset, obtained using an experimentally well validated numerical model, is used to develop the ML models: deep artificial neural network (ANN) and decision tree algorithms such as Random Forest (RF) and extreme gradient boosting (XGB). The structures of these models are modified by cascading the output features according to the physics involved. This distinctive approach results in better prediction of the evaporation kinetics than basic ML models. The models are trained by a large set of input parameters for the target variables: viz. droplet evaporation rate, velocity scale, and temperature drop in the solid and liquid domain. Finally, the performance of these ML models is assessed by comparing their predictions with that of physics-based, experimentally validated, numerical models. Results show that the inclusion of additional features obtained using feature engineering significantly improve the prediction performance of ML algorithms, and consistently accurate predictions of droplet evaporation kinetics are obtained. Among various algorithms considered here, the ANN outperforms in term of various error matrices for most of the cases, followed by XGB, and RF models. Also, the highest mean average error (MAE) yield by the ML models for evaporation rate, velocity scale and temperature drop in liquid remains within ∼12.5%. In the case of temperature drop for the solid, the MAE is considerably higher due to large variability of the same target variable. Overall, the work clearly shows that ML algorithms can be used to obtain physically consistent predictions for sessile droplet evaporation parameters.

Achieving precise multiparameter measurements with distributed optical fiber sensor using wavelength diversity and deep neural networks

The development of advanced distributed optical fiber sensing systems that are capable of performing accurate and spatially resolved multiparameter measurements is of great interest to a wide range of scientific and industrial applications. Here, we propose and experimentally demonstrate a wavelength diversity based advanced distributed optical fiber sensor system to accomplish multiparameter sensing while greatly enhancing measurement accuracy. A suite of deep neural network (DNN) algorithms are developed and verified for data denoising, rapid Brillouin frequency shift estimation, and vibration data event classification. As a proof-of-concept, we demonstrate the effectiveness of the proposed advanced wavelength diversity distributed fiber sensor system assisted by DNN for simultaneous, independent measurements of static strain, temperature, and acoustic vibrations over a 25 km long sensing fiber at 3 m spatial resolution. These results suggest the potential for an intelligent multiparameter monitoring system with enhanced performance in advanced structural health monitoring applications.

Deep Convolutional Neural Network Algorithm Based on Optical Sensors and Wireless Mobile Networks for Real time Monitoring of Physical Health

Deep Convolutional Neural Network Algorithm Based on Optical Sensors and Wireless Mobile Networks for Real time Monitoring of Physical Health

Detecting failed tethers in submerged floating tunnels using an LSTM autoencoder and DNN algorithms

This study proposes a two-step approach for detecting damaged tethers in submerged floating tunnels. The proposed method employs two different artificial neural network algorithms. First, the long short-term memory (LSTM) autoencoder model trained using response datasets under intact conditions was used to reconstruct the measured acceleration data of the target structure. Further, the data reconstruction error was used as the input for the deep neural network algorithm trained in advance using the reconstruction error pattern in various tether damage cases. The proposed method was verified by conducting a well-validated simulation based on hydrodynamics. The damage-detection accuracy of the proposed method was directly compared with that of a conventional supervised learning algorithm-based approach. In addition, the case study results confirmed that the proposed approach was applicable to other submerged floating tunnel (SFT) structures by retraining the LSTM autoencoder and deep neural network algorithms with intact datasets only. Thus, this approach does not require a large amount of training data or simulation model updates for other SFT structures.

Automatic recognition of active landslides by surface deformation and deep learning

Catastrophic landslides are generally evolved from potential active landslides, and early identification of active landslides over an extensive region is vital to effective prevention and control of disastrous landslides in urban areas. Interferometric Synthetic Aperture Radar (InSAR) has immense potential in mapping active landslides. However, artificial interpretation of InSAR measurements and manual recognition of active landslides are very laborious and time-consuming, with a relatively high missing and false alarms. That hinders the application of InSAR technique and the identification of active landslides in wide areas. Automatic recognition of active landslides has always been a great challenge and has been relatively rarely investigated by previous studies. This work establishes comprehensive identification indices of geoenvironmental, disaster-triggering, and surface deformation features. Moreover, it suggests a novel deep learning algorithm of SDeepFM to conduct automatic identification of active landslides across a vast and landslide-serious area of Hualong County. Some new viewpoints are suggested as follows. (1) The identification indices consist of disaster-controlling, disaster-inducing, and active deformation characteristics and are constructed in terms of the cause characteristics of active landslides. Thus, it can effectively decrease the false alarms of active landslide identification. (2) The proposed SDeepFM algorithm features a spatial-perception ability and can adequately extract and fuse the low-level and high-level semantic features. It outperforms the classification and regression tree (CART), multi-layer perceptron (MLP), convolutional neural network (CNN), and deep neural network (DNN) algorithms. The test accuracy attains 0.91, 99.73%, 90.21%, 0.92, 0.96, and 0.91 in F1-score, Accuracy, Precision, Recall, AUC, and Kappa, respectively. (3) A total of 164 active landslides are exactly recognized, and 39 active landslides are newly identified in this work. (4) In Hualong County, the characteristics of slope deformation, spatial context, lithology, tectonic movement, human activity, and topography play important roles in active landslide identification. River distribution and rainfall also contribute to active landslide recognition.

On the application of hybrid deep 3D convolutional neural network algorithms for predicting the micromechanics of brain white matter

Background:Material characterization of brain white matter (BWM) is difficult due to the anisotropy inherent to the three-dimensional microstructure and the various interactions between heterogeneous brain-tissue (axon, myelin, and glia). Developing full scale finite element models that accurately represent the relationship between the micro and macroscale BWM is however extremely challenging and computationally expensive. The anisotropic properties of the microstructure of BWM computed by building unit cells under frequency domain viscoelasticity comprises of 36 individual constants each, for the loss and storage moduli. Furthermore, the architecture of each unit cell is arbitrary in an infinite dataset. Methods:In this study, we extend our previous work on developing representative volume elements (RVE) of the microstructure of the BWM in the frequency domain to develop 3D deep learning algorithms that can predict the anisotropic composite properties. The deep 3D convolutional neural network (CNN) algorithms utilizes a voxelization method to obtain geometry information from 3D RVEs. The architecture information encoded in the voxelized location is employed as input data while cross-referencing the RVEs’ material properties (output data). We further develop methods by incorporating parallel pathways, Residual Neural Networks and inception modulus that improve the efficiency of deep learning algorithms. Results:This paper presents different CNN algorithms in predicting the anisotropic composite properties of BWM. A quantitative analysis of the individual algorithms is presented with the view of identifying optimal strategies to interpret the combined measurements of brain MRE and DTI. Significance:The proposed Multiscale 3D ResNet (M3DR) algorithm demonstrates high learning ability and performance over baseline CNN algorithms in predicting BWM tissue properties. The hybrid M3DR framework also overcomes the significant limitations encountered in modeling brain tissue using finite elements alone including those such as high computational cost, mesh and simulation failure. The proposed framework also provides an efficient and streamlined platform for implementing complex boundary conditions, modeling intrinsic material properties and imparting interfacial architecture information.

Ptychographic phase retrieval via a deep-learning-assisted iterative algorithm.

Ptychography is a powerful computational imaging technique with microscopic imaging capability and adaptability to various specimens. To obtain an imaging result, it requires a phase-retrieval algorithm whose performance directly determines the imaging quality. Recently, deep neural network (DNN)-based phase retrieval has been proposed to improve the imaging quality from the ordinary model-based iterative algorithms. However, the DNN-based methods have some limitations because of the sensitivity to changes in experimental conditions and the difficulty of collecting enough measured specimen images for training the DNN. To overcome these limitations, a ptychographic phase-retrieval algorithm that combines model-based and DNN-based approaches is proposed. This method exploits a DNN-based denoiser to assist an iterative algorithm like ePIE in finding better reconstruction images. This combination of DNN and iterative algorithms allows the measurement model to be explicitly incorporated into the DNN-based approach, improving its robustness to changes in experimental conditions. Furthermore, to circumvent the difficulty of collecting the training data, it is proposed that the DNN-based denoiser be trained without using actual measured specimen images but using a formula-driven supervised approach that systemically generates synthetic images. In experiments using simulation based on a hard X-ray ptychographic measurement system, the imaging capability of the proposed method was evaluated by comparing it with ePIE and rPIE. These results demonstrated that the proposed method was able to reconstruct higher-spatial-resolution images with half the number of iterations required by ePIE and rPIE, even for data with low illumination intensity. Also, the proposed method was shown to be robust to its hyperparameters. In addition, the proposed method was applied to ptychographic datasets of a Simens star chart and ink toner particles measured at SPring-8 BL24XU, which confirmed that it can successfully reconstruct images from measurement scans with a lower overlap ratio of the illumination regions than is required by ePIE and rPIE.

Liquid thermophysical properties of Ag-Si alloy based on deep learning potential

The knowledge of the thermophysical properties of liquid metals and alloys is essential for expanding the materials database and designing materials with good properties. In this work, we developed an interatomic potential using a deep neural network (DNN) algorithm for liquid Ag-Si alloys. Compared with ab initio molecular dynamics (AIMD) results, the DNN potential provided a good description of the information of energy, force, and structure features of the system in the simulated temperature range. Through this potential, we can obtain the thermophysical properties of different compositions of liquid alloys by simulation way. The computed thermophysical properties are in excellent agreement with the reported experimental data. The analysis of local structure indicates that the liquid ordering and stability strengthen upon cooling at the atomic level, eventually leading to an increase in thermophysical properties.