Deep Learning Neural Network Model Research Articles

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

Forecasting Gas Well Classification Based on a Two-Dimensional Convolutional Neural Network Deep Learning Model

In response to the limitations of existing evaluation methods for gas well types in tight sandstone gas reservoirs, characterized by low indicator dimensions and a reliance on traditional methods with low prediction accuracy, therefore, a novel approach based on a two-dimensional convolutional neural network (2D-CNN) is proposed for predicting gas well types. First, gas well features are hierarchically selected using variance filtering, correlation coefficients, and the XGBoost algorithm. Then, gas well types are determined via spectral clustering, with each gas well labeled accordingly. Finally, the selected features are inputted, and classification labels are outputted into the 2D-CNN, where convolutional layers extract features of gas well indicators, and the pooling layer, which, trained by the backpropagation of CNN, performs secondary dimensionality reduction. A 2D-CNN gas well classification prediction model is constructed, and the softmax function is employed to determine well classifications. This methodology is applied to a specific tight gas reservoir. The study findings indicate the following: (1) Via two rounds of feature selection using the new algorithm, the number of gas well indicator dimensions is reduced from 29 to 15, thereby reducing the computational complexity of the model. (2) Gas wells are categorized into high, medium, and low types, addressing a deep learning multi-class prediction problem. (3) The new method achieves an accuracy of 0.99 and a loss value of 0.03, outperforming BP neural networks, XGBoost, LightGBM, long short-term memory networks (LSTMs), and one-dimensional convolutional neural networks (1D-CNNs). Overall, this innovative approach demonstrates superior efficacy in predicting gas well types, which is particularly valuable for tight sandstone gas reservoirs.

Deep learning radiomics-based prediction model of metachronous distant metastasis following curative resection for retroperitoneal leiomyosarcoma: a bicentric study

BackgroundCombining conventional radiomics models with deep learning features can result in superior performance in predicting the prognosis of patients with tumors; however, this approach has never been evaluated for the prediction of metachronous distant metastasis (MDM) among patients with retroperitoneal leiomyosarcoma (RLS). Thus, the purpose of this study was to develop and validate a preoperative contrast-enhanced computed tomography (CECT)-based deep learning radiomics model for predicting the occurrence of MDM in patients with RLS undergoing complete surgical resection.MethodsA total of 179 patients who had undergone surgery for the treatment of histologically confirmed RLS were retrospectively recruited from two tertiary sarcoma centers. Semantic segmentation features derived from a convolutional neural network deep learning model as well as conventional hand-crafted radiomics features were extracted from preoperative three-phase CECT images to quantify the sarcoma phenotypes. A conventional radiomics signature (RS) and a deep learning radiomics signature (DLRS) that incorporated hand-crafted radiomics and deep learning features were developed to predict the risk of MDM. Additionally, a deep learning radiomics nomogram (DLRN) was established to evaluate the incremental prognostic significance of the DLRS in combination with clinico-radiological predictors.ResultsThe comparison of the area under the curve (AUC) values in the external validation set, as determined by the DeLong test, demonstrated that the integrated DLRN, DLRS, and RS models all exhibited superior predictive performance compared with that of the clinical model (AUC 0.786 [95% confidence interval 0.649–0.923] vs. 0.822 [0.692–0.952] vs. 0.733 [0.573–0.892] vs. 0.511 [0.359–0.662]; both P < 0.05). The decision curve analyses graphically indicated that utilizing the DLRN for risk stratification provided greater net benefits than those achieved using the DLRS, RS and clinical models. Good alignment with the calibration curve indicated that the DLRN also exhibited good performance.ConclusionsThe novel CECT-based DLRN developed in this study demonstrated promising performance in the preoperative prediction of the risk of MDM following curative resection in patients with RLS. The DLRN, which outperformed the other three models, could provide valuable information for predicting surgical efficacy and tailoring individualized treatment plans in this patient population.Trial registration: Not applicable.

Convolutional neural network deep learning model accurately detects rectal cancer in endoanal ultrasounds

BackgroundImaging is vital for assessing rectal cancer, with endoanal ultrasound (EAUS) being highly accurate in large tertiary medical centers. However, EAUS accuracy drops outside such settings, possibly due to varied examiner experience and fewer examinations. This underscores the need for an AI-based system to enhance accuracy in non-specialized centers. This study aimed to develop and validate deep learning (DL) models to differentiate rectal cancer in standard EAUS images.MethodsA transfer learning approach with fine-tuned DL architectures was employed, utilizing a dataset of 294 images. The performance of DL models was assessed through a tenfold cross-validation.ResultsThe DL diagnostics model exhibited a sensitivity and accuracy of 0.78 each. In the identification phase, the automatic diagnostic platform achieved an area under the curve performance of 0.85 for diagnosing rectal cancer.ConclusionsThis research demonstrates the potential of DL models in enhancing rectal cancer detection during EAUS, especially in settings with lower examiner experience. The achieved sensitivity and accuracy suggest the viability of incorporating AI support for improved diagnostic outcomes in non-specialized medical centers.

Deep Learning Neural Network (DNN) Coupled Laser Induced Breakdown Spectroscopy for Quantitative Analysis of Copper Alloys

Conventional Laser Induced Breakdown Spectroscopy (LIBS) quantitative analysis, employing both calibration and calibration-free techniques, is challenged by spectral overlapping, self-absorption, and spectral broadening effects, leading to decreased accuracy. Recently, integration of machine learning (ML) algorithms with LIBS has been increasingly employed to tackle these challenges. This article explores the augmentation of LIBS with deep learning neural networks (DNN) to enhance accuracy of quantitative analysis of multi-elemental copper alloys. Sufficient training data was acquired by simulating optical emission spectra for bronze (Cu-Sn) and admiralty brass (Cu-Zn-Sn) alloys under standard laser produced plasmas conditions, encompassing different alloy concentrations, electron temperatures, and densities. We designed a regularized DNN structure, fine-tuned using a validation dataset to optimize quantitative results. The model’s accuracy was assessed with test dataset. The quantitative results demonstrated reduced loss as training spectra increased from 500 to 5000 for both alloys. The decline in mean squared error, from 2.793×10−3 to 4.283×10−5 for bronze and from 3.245×10−2 to 5.598×10−4 for admiralty brass alloys, as training data increased from 500 to 5000, underscores the proposed DNN model’s potential for metallurgical alloy quantification.

Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma

PurposeTo investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC).Materials and MethodsFrom June 2019 to October 2022, 617 patients with ILADC who underwent preoperative chest CT scans in our institution were randomly placed into training and internal validation sets in a 4:1 ratio, and 353 patients with ILADC from another institution were included as an external validation set. Then, a self-paced learning (SPL) 3D Net was used to establish two DL models: model 1 was used to predict the M/S growth pattern in ILADC, and model 2 was used to predict that pattern in ≤ 2-cm-diameter ILADC.ResultsFor model 1, the training cohort’s area under the curve (AUC), accuracy, recall, precision, and F1-score were 0.924, 0.845, 0.851, 0.842, and 0.843; the internal validation cohort’s were 0.807, 0.744, 0.756, 0.750, and 0.743; and the external validation cohort’s were 0.857, 0.805, 0.804, 0.806, and 0.804, respectively. For model 2, the training cohort’s AUC, accuracy, recall, precision, and F1-score were 0.946, 0.858, 0.881,0.844, and 0.851; the internal validation cohort’s were 0.869, 0.809, 0.786, 0.794, and 0.790; and the external validation cohort’s were 0.831, 0.792, 0.789, 0.790, and 0.790, respectively. The SPL 3D Net model performed better than the ResNet34, ResNet50, ResNeXt50, and DenseNet121 models.ConclusionThe CT-based DL model performed well as a noninvasive screening tool capable of reliably detecting and distinguishing the subtypes of ILADC, even in small-sized tumors.

Progression of COVID-19 Cases in Telangana State by using ARIMA, MLP, ELM and LSTM Prediction Models by Retrospective Confirmation

Objective: The importance of this research article is to evaluate efficient model for diagnosing pandemic COVID-19 positive cases in Telangana State, India. Method: Neural Network models (Extreme Learning Machine and Multi-Layer Perception), Deep Learning Neural Network model (Long Short Term Memory-LSTM) and traditional Auto Regressive Integrated Moving Average (ARIMA) models were applied and the data was converted from non-linear to linear (stationarity) for forecasting Covid-19 positive cases. The study of the data covered from 1st. Dec 2020 to 30th May 2021. 80% of train data was taken to fit the models and then 20% of the test data was used to predict the values. The deviation between original test data and predicted data led to an error. Among these error values, the model which had minimum errors was considered as the best of the four models. Findings: LSTM model was proved to be the most efficient model, as a result of the least Root mean square error (RMSE = 71.12) compared to ARIMA (258.20), ELM (553.67) and MLP (641.86) values. Novelty: These forecasting methods succour to predict the Covid-19 positive cases in the forthcoming days. This has been suggested for taking the better preventive steps to control the Covid-19 positive cases. Keywords: COVID­19, ARIMA, LSTM, MLP, ELM Forecasting

Short term power load forecasting system based on improved neural network deep learning model

AbstractThe electricity load prediction is closely related to production and daily life. The electricity load prediction is also a very important task. With the widespread application of smart grids, load data shows an exponential growth trend. The huge amount of data in the load makes power prediction even more difficult. On the basis of traditional prediction algorithms, a power load prediction model based on machine learning and neural networks is designed. Because the single model prediction has the unstable results, a combined model is obtained based on the ensemble learning idea and two single model prediction method. The prediction results are detected by the load data. From the experimental results, the mean absolute percentage error (MAPE) of the AdaBoost‐GRU data fusion model is 0.066%. Compared to the AdaBoost‐GRU data fusion model, the MAPE decreases by 1.59% and 1.12%, respectively. The relative mass scores of the two groups decrease by 132.57% and 89.14%, respectively. The prediction accuracy is improved, which has advantages compared to traditional combination models. It can effectively enhance the accuracy of short‐term power grid load forecasting. It is an important scientific and practical reference for power grid decision‐making.

Deep learning-based forecasting model for chlorophyll-a response to tropical cyclones in the Western North Pacific

Tropical cyclones cause increases in sea surface chlorophyll-a concentration, which is important for studying variations in the regional marine environment. Precisely forecasting the variations of sea surface chlorophyll-a concentration induced by tropical cyclones remains a challenge. In this research, a bidirectional long short-term memory (BiLSTM) neural network deep learning model was applied to predict the variations of sea surface chlorophyll-a concentration induced by typhoons in the Western North Pacific (WNP). Typhoons occurring between 2011 and 2020 were used as training cases and those from 2021 to 2022 as forecasting and testing cases. The input variables of the deep learning model include the sea surface wind at 10 meters (U10 and V10), sea surface temperature anomaly (SSTA), and sea surface chlorophyll-a concentration. The output variable was the chlorophyll-a concentration one day after the passage of the typhoon. Data from the previous 7 days were used to predict the chlorophyll-a concentration one day after the typhoon's passage, and the rolling forecast method was employed to predict chlorophyll-a concentration in the following 7 days. To assess the impact of input variables on the model's forecasting performance, ablation experiments were conducted. The results showed that when using U10, V10, and chlorophyll-a from the previous seven days as inputs, the model exhibited the best overall forecasting performance. Taking Typhoons Chanthu, In-fa, and Malou as examples, the root mean square error (RMSE) for the forecast results are 0.0143 mg · m−3, 0.0087 mg · m−3, and 0.0030 mg · m−3, the mean absolute errors (MAE) are 0.0072 mg · m−3, 0.0074 mg · m−3, and 0.0025 mg · m−3, and the spatial anomaly correlation coefficients (ACC) are 0.9968, 0.9775, and 0.9721, respectively. The results reveal that the most accurate forecasting performance was observed during the mid-phase of the moderate-intensity Typhoon Muifa, with RMSE, MAE, and ACC values of 0.0040 mg · m−3, 0.0032 mg · m−3, and 0.9894, respectively. The BiLSTM neural network model had the best forecasting performance for typhoons of moderate intensity and during the mid-term phase. This is because moderate-intensity typhoons or the mature phase of any typhoon tend to have relatively stable and more predictable paths, resulting in better predictions of chlorophyll-a concentrations. In future work, we intend to extend our training and forecasting to typhoons of various intensities, aiming to further refine and enhance predictive performance.

Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models

IntroductionColorectal cancer (CRC), also known as colorectal cancer, is a significant disease marked by high fatality rates, ranking as the third leading cause of global mortality. The main objective of this study was to assess the accuracy of predictive models in predicting both mortality events and the probability of disease recurrence. MethodA retrospective analysis was conducted on a cohort of 284 individuals diagnosed with colorectal cancer between 2001 and 2017. Demographic and clinical data, including gender, disease stage, age at diagnosis, recurrence status, and treatment details, were meticulously recorded. We rigorously evaluated various predictive models, including Decision Trees, Random Forests, Random Survival Forests (RSF), Gradient Boosting, mboost, Deep Learning Neural Network (DLNN), and Cox regression. Performance metrics, such as sensitivity, positive predictive value (PPV), specificity, area under the receiver operating characteristic curve (ROC area), and overall accuracy, were calculated for each model to predict mortality and disease recurrence. The analysis was performed using R version 4.1.3 software and the Python programming language. ResultsFor mortality prediction, the mboost model demonstrated the highest sensitivity at 96.9% (95% CI: 0.83–0.99) and an ROC area of 0.88. It also exhibited high specificity at 80% (95% CI: 0.59–0.93), a positive predictive value of 86.1% (95% CI: 0.70–0.95), and an overall accuracy of 89% (95% CI: 0.78–0.96). Random Forests showed perfect sensitivity of 100% (95% CI: 0.85–1) but had low specificity at 0% (95% CI: 0–0.52) and poor overall accuracy (50%). On the other hand, DLNN had the lowest performance metrics for mortality prediction, with a sensitivity of 24% (95% CI: 0.222–0.268), specificity of 75% (95% CI: 0.73–0.77), and a lower positive predictive value of 42% (95% CI: 0.38–0.45). The Gradient Boosting model showed the best performance in predicting recurrence, achieving perfect sensitivity of 100% (95% CI: 0.87–1) and high specificity at 92.9% (95% CI: 0.76–0.99). It also had a high positive predictive value of 93.3% (95% CI: 0.77–0.99). Gradient Boosting, with an ROC area of 96.4%, and mboost, with an ROC area of 75%, demonstrated remarkable performance. DLNN had the lowest performance metrics for recurrence prediction, with sensitivity at 1.75% (95% CI: 0.01–0.02), specificity at 98% (95% CI: 0.97–0.98), and a lower positive predictive value at 52.6% (95% CI: 0.39–0.65). ConclusionIn summary, the mboost model demonstrated outstanding performance in predicting mortality, achieving exceptional results across various evaluation metrics. Random Forests exhibited perfect sensitivity but showed poor specificity and overall accuracy. The DLNN model displayed the lowest performance metrics for mortality prediction. In terms of recurrence prediction, the Gradient Boosting model outperformed other models with perfect sensitivity, high specificity, and positive predictive value. The DLNN model had the lowest performance metrics for recurrence prediction. Overall, the results emphasize the effectiveness of the mboost and Gradient Boosting models in predicting mortality and recurrence in colorectal cancer patients.

White matter brain age as a biomarker of cerebrovascular burden in the ageing brain.

As the brain ages, it almost invariably accumulates vascular pathology, which differentially affects the cerebral white matter. A rich body of research has investigated the link between vascular risk factors and the brain. One of the less studied questions is that among various modifiable vascular risk factors, which is the most debilitating one for white matter health? A white matter specific brain age was developed to evaluate the overall white matter health from diffusion weighted imaging, using a three-dimensional convolutional neural network deep learning model in both cross-sectional UK biobank participants (n = 37,327) and a longitudinal subset (n = 1409). White matter brain age gap (WMBAG) was the difference between the white matter age and the chronological age. Participants with one, two, and three or more vascular risk factors, compared to those without any, showed an elevated WMBAG of 0.54, 1.23, and 1.94years, respectively. Diabetes was most strongly associated with an increased WMBAG (1.39years, p < 0.001) among all risk factors followed by hypertension (0.87years, p < 0.001) and smoking (0.69years, p < 0.001). Baseline WMBAG was associated significantly with processing speed, executive and global cognition. Significant associations of diabetes and hypertension with poor processing speed and executive function were found to be mediated through the WMBAG. White matter specific brain age can be successfully targeted for the examination of the most relevant risk factors and cognition, and for tracking an individual's cerebrovascular ageing process. It also provides clinical basis for the better management of specific risk factors.

Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data

The diagnosis of severe COVID-19 lung infection is important because it carries a higher risk for the patient and requires prompt treatment with oxygen therapy and hospitalization while those with less severe lung infection often stay on observation. Also, severe infections are more likely to have long-standing residual changes in their lungs and may need follow-up imaging. We have developed deep learning neural network models for classifying severe vs. non-severe lung infections in COVID-19 patients on chest radiographs (CXR). A deep learning U-Net model was developed to segment the lungs. Inception-v1 and Inception-v4 models were trained for the classification of severe vs. non-severe COVID-19 infection. Four CXR datasets from multi-country and multi-institutional sources were used to develop and evaluate the models. The combined dataset consisted of 5748 cases and 6193 CXR images with physicians’ severity ratings as reference standard. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. We studied the reproducibility of classification performance using the different combinations of training and validation data sets. We also evaluated the generalizability of the trained deep learning models using both independent internal and external test sets. The Inception-v1 based models achieved AUC ranging between 0.81 ± 0.02 and 0.84 ± 0.0, while the Inception-v4 models achieved AUC in the range of 0.85 ± 0.06 and 0.89 ± 0.01, on the independent test sets, respectively. These results demonstrate the promise of using deep learning models in differentiating COVID-19 patients with severe from non-severe lung infection on chest radiographs.

Who can benefit from postmastectomy radiotherapy among HR+/HER2- T1-2 N1M0 breast cancer patients? An explainable machine learning mortality prediction based approach.

The necessity of postmastectomy radiotherapy(PMRT) for patients with HR+/HER2 T1-2 N1M0 breast cancer remains controversial. We want to use explainable machine learning to learn the feature importance of the patients and identify the subgroup of the patients who may benefit from the PMRT. Additionally, develop tools to provide guidance to the doctors. In this study, we trained and validated 2 machine learning survival models: deep learning neural network and Cox proportional hazard model. The training dataset consisted of 35,347 patients with HR+/HER2- T1-2 N1M0 breast cancer who received mastectomies from the SEER database from 2013 to 2018. The performance of survival models were assessed using a concordance index (c-index).Then we did subgroup analysis to identify the subgroup who could benefit from PMRT. We also analyzed the global feature importance for the model and individual feature importance for individual survival prediction. Finally, we developed a Cloud-based recommendation system for PMRT to visualize the survival curve of each treatment plan and deployed it on the Internet. A total of 35,347 patients were included in this study. We identified that radiotherapy improved the OS in patients with tumor size >14mm and age older than 54: 5-year OS rates of 91.9 versus 87.2% (radio vs. nonradio, P <0.001) and cohort with tumor size >14mm and grade worse than well-differentiated, 5-year OS rates of 90.8 versus 82.3% (radio vs. nonradio, P <0.001).The deep learning network performed more stably and accurately in predicting patients survival than the random survival forest and Cox proportional hazard model on the internal test dataset (C-index=0.776 vs 0.641) and in the external validation(C-index=0.769 vs 0.650).Besides, the deep learning model identified several key factors that significantly influence patient survival, including tumor size, examined regional nodes, age at 45-49 years old and positive regional nodes (PRN). Patients with tumor size >14mm and age older than 54 and cohort with tumor size >14mm and grade worse than well-differentiated could benefit from the PMRT. The deep learning network performed more stably and accurately in predicting patients survival than Cox proportional hazard model on the internal test. Besides, tumor size, examined regional nodes, age at 45-49 years old and PRN are the most significant factors to the overall survival (OS).

Clinical Application of an Artificial Intelligence System for Diagnosing Thyroid Disease Based on a Computer Neural Network Deep Learning Model.

This study aimed to establish a stereoscopic neural learning network through deep learning and construct an artificial intelligence (AI) diagnosis system for the prediction of benign and malignant thyroid diseases, as well as repeatedly verified the diagnosis system and adjusted the data, in order to develop a type of AI-assisted thyroid diagnosis software with a low false negative rate and high sensitivity for clinical practice. From July 2020 to April 2023, A total of 36 patients with thyroid nodules in our hospital were selected for diagnosis of thyroid nodules based on the Expert Consensus on Thyroid Ultrasound; samples were taken by aspiration biopsy or surgically and sent for pathological diagnosis. The ultrasonic diagnosis results were compared with the pathological results, a database was established based on the ultrasonic diagnostic characteristics and was entered in the AI-assisted diagnosis software for judgment of benign and malignant conditions. The data in the software were corrected based on the conformity rate and the reasons for misjudgment, and the corrected software was used to evaluate the benign and malignant conditions of the 36 patients, until the conformity rate exceeded 90%. The initial conformity rate of the AI software for identifying benign and malignant conditions was 88%, while that of the software utilizing the database was 94%. We established a stereoscopic neural learning network and construct an AI diagnosis system for the prediction of benign and malignant thyroid diseases, with a low false negative rate and high sensitivity for clinical practice.

A quality control system for logistic ports goods movable harbor cranes based on internet of things and deep learning

&lt;span&gt;The growth of commercial activity and the transportation of goods around the world has increased the challenges of stevedoring within ports. In case of loading and unloading ships safely, quickly, and efficiently, goods movable harbor cranes play an important role. This work aims to propose an industrial internet of things (IIoT)-based quality control system for logistic services ports goods movable harbor crane (QC-GMHC). The GMHC system based on using programmable logic controller (PLC), along with a multi-sensor data collecting system. Several operations have been done to establish the QC-GMHC system as: GMHC sensors real-time data storage, and data sharing; monitoring the GMHC status (remote-local); and the efficiency reporting. In order to validate the proposed system’s hardware, it was used in an already operational GMHC for six months, during which data were collected and analyzed. The results revealed that the proposed hardware system worked efficiently for 24 hours. To forecast the efficiency of the GMHC, a deep learning (DL) conventional long short-term memory (LSTM) and neural network model was trained and validated using synthetic data generated from acquired real data. The results showed that QC-GMHC can calculate efficiency with an accuracy of 80%, which is sufficient for our application.&lt;/span&gt;

Automatic segmentation of ameloblastoma on ct images using deep learning with limited data

BackgroundAmeloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures.MethodsWe collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance.ResultsDuring extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance.ConclusionIn this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment.

Retracted: Fully Convolutional Neural Network Deep Learning Model Fully in Patients with Type 2 Diabetes Complicated with Peripheral Neuropathy by High-Frequency Ultrasound Image.

[This retracts the article DOI: 10.1155/2022/5466173.].

Wind Turbine Predictive Fault Diagnostics Based on a Novel Long Short-Term Memory Model

The operation and maintenance (O&amp;M) issues of offshore wind turbines (WTs) are more challenging because of the harsh operational environment and hard accessibility. As sudden component failures within WTs bring about durable downtimes and significant revenue losses, condition monitoring and predictive fault diagnostic approaches must be developed to detect faults before they occur, thus preventing durable downtimes and costly unplanned maintenance. Based primarily on supervisory control and data acquisition (SCADA) data, thirty-three weighty features from operational data are extracted, and eight specific faults are categorised for fault predictions from status information. By providing a model-agnostic vector representation for time, Time2Vec (T2V), into Long Short-Term Memory (LSTM), this paper develops a novel deep-learning neural network model, T2V-LSTM, conducting multi-level fault predictions. The classification steps allow fault diagnosis from 10 to 210 min prior to faults. The results show that T2V-LSTM can successfully predict over 84.97% of faults and outperform LSTM and other counterparts in both overall and individual fault predictions due to its topmost recall scores in most multistep-ahead cases performed. Thus, the proposed T2V-LSTM can correctly diagnose more faults and upgrade the predictive performances based on vanilla LSTM in terms of accuracy, recall scores, and F-scores.

Reconstruction of Substructural Transfer Functions with Operational Responses Using Deep Learning Approach

This study aims to develop an identification method of substructural FRFs without disassembling the whole system into substructures by measuring operational responses at input and target points. To identify the FRFs between input and target points in a substructure, the relationship of operational responses in the whole system is re-formulated. Then, by measuring operational responses, the FRFs can be identified by a data-driven approach. Deep learning neural network models are introduced to reconstruct the transfer function. To validate the proposed formulation, a test structure that consists of two substructures and three spring joints is fabricated. Operational responses of the test structure are measured when an exciter randomly excites the test structure. Dense neural networks (DNNs) are developed to reconstruct the FRFs between the joints and target points. The identified FRFs are compared with those of directly measured substructural FRFs to estimate the performance of the proposed formulation.

A Predictive Model Using Deep Learning Neural Network for Efficient Intrusion Detection

Abstract: Network intrusion detection system helps to detect exploitations and mitigate damages. A network intrusion detection system detects the network traffic that deviates from the normal behavioral pattern. Developing an efficient intrusion detection system has many challenges and the patterns associated with one type of intrusion differ from other intrusions. In such situations, understanding different patterns and differentiating intrusions becomes essential to detect anomalies and attacks in the network. Deep learning models offer more power and intelligence to the detection system and extend the ability to differentiate &amp; understand the network feature characteristics, also machine learning models with feature selection showed high performance in intrusion detections. This paper evaluates the proposed deep learning neural network model and machine learning models using feature selection for efficient intrusion detection using real world dataset.