Deep Learning-based Prediction Model Research Articles

Analysing and Evaluating the Performance of Deep-Learning-Based Arrhythmia Detection Using Electrocardiogram Signals

Cardiac arrhythmia is a cardiac irregularity that impacts a significant number of individuals globally. Certain arrhythmias may be benign or occur only once, while recurring arrhythmias have the potential to cause organ failure, increase the risk of stroke by a factor of five, and even lead to sudden cardiac death. Thus, in order to identify and treat arrhythmia and prevent potentially fatal cardiac problems, the rapid detection and categorization of Electrocardiogram (ECG) signals is of paramount importance. The non-invasive technique employs electrodes to examine the electrical potentials of the heart, facilitating the detection of structural and functional irregularities that contribute to the diagnosis of cardiovascular illnesses. Nevertheless, the high likelihood of manual interpretation, which is both time-consuming and susceptible to error caused by weariness, poses a significant challenge for cardiologists in identifying and diagnosing cardiac issues. In recent times, there has been a growing utilization of Deep Learning (DL) models in the field of arrhythmia prediction, with the aim of enhancing clinical decision-making and potentially mitigating the likelihood of valetudinarian fatality. These models enhance the diagnostic capabilities of electrocardiography by detecting pathological situations, extracting anatomically meaningful data, evaluating cardio-motion, and assessing the quality of echo images. As a result, they serve as an alternative tool for precise diagnosis and treatment of arrhythmias. In order to forecast and categorize arrhythmias based on ECG signal data, this study provides a comprehensive review of various deep learning approaches. The initial section of the study provides a concise overview of various deep learning-based prediction models that have been developed by multiple researchers for echocardiography systems. Subsequently, a comparative analysis is undertaken to comprehend the limitations of those algorithms and propose a novel approach to improve the accuracy of cardiac view classification in echocardiography systems.

Deep learning derived input function in dynamic [18F]FDG PET imaging of mice

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep learning based prediction model (DLIF), that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F]Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice.

A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs.

Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.

Benchmarking machine learning-based real-time respiratory signal predictors in 4D SBRT.

Stereotactic body radiotherapy of thoracic and abdominal tumors has to account for respiratory intrafractional tumor motion. Commonly, an external breathing signal is continuously acquired that serves as a surrogate of the tumor motion and forms the basis of strategies like breathing-guided imaging and gated dose delivery. However, due to inherent system latencies, there exists a temporal lag between the acquired respiratory signal and the system response. Respiratory signal prediction models aim to compensate for the time delays and to improve imaging and dosedelivery. The present study explores and compares six state-of-the-art machine and deep learning-based prediction models, focusing on real-time and real-world applicability. All models and data are provided as open source and data to ensure reproducibility of the results and fosterreuse. The study was based on 2502 breathing signals ( h) acquired during clinical routine, split into independent training (50%), validation (20%), and test sets (30%). Input signal values were sampled from noisy signals, and the target signal values were selected from corresponding denoised signals. A standard linear prediction model (Linear), two state-of-the-art models in general univariate signal prediction (Dlinear, Xgboost), and three deep learning models (Lstm, Trans-Enc, Trans-TSF) were chosen. The prediction performance was evaluated for three different prediction horizons (480, 680, and 920ms). Moreover, the robustness of the different models when applied to atypical, that is, out-of-distribution (OOD) signals, was analyzed. The Lstm model achieved the lowest normalized root mean square error for all prediction horizons. The prediction errors only slightly increased for longer horizons. However, a substantial spread of the error values across the test signals was observed. Compared to typical, that is, in-distribution test signals, the prediction accuracy of all models decreased when applied to OOD signals. The more complex deep learning models Lstm and Trans-Enc showed the least performance loss, while the performance of simpler models like Linear dropped the most. Except for Trans-Enc, inference times for the different models allowed for real-time application. The application of the Lstm model achieved the lowest prediction errors. Simpler prediction filters suffer from limited signal history access, resulting in a drop in performance for OODsignals.

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.

Deep Learning-Based Prediction Model for Gait Recovery after a Spinal Cord Injury.

Predicting gait recovery after a spinal cord injury (SCI) during an acute rehabilitation phase is important for planning rehabilitation strategies. However, few studies have been conducted on this topic to date. In this study, we developed a deep learning-based prediction model for gait recovery after SCI upon discharge from an acute rehabilitation facility. Data were collected from 405 patients with acute SCI admitted to the acute rehabilitation facility of Korea University Anam Hospital between June 2008 and December 2022. The dependent variable was Functional Ambulation Category at the time of discharge (FAC-DC). Seventy-one independent variables were selected from the existing literature: basic information, International Standards for Neurological Classification of SCI scores, neurogenic bladders, initial FAC, and somatosensory-evoked potentials of the lower extremity. Recurrent neural network (RNN), linear regression (LR), Ridge, and Lasso methods were compared for FAC-DC prediction in terms of the root-mean-squared error (RMSE). RNN variable importance, which is the RMSE gap between a complete RNN model and an RNN model excluding a certain variable, was used to evaluate the contribution of this variable. Based on the results of this study, the performance of the RNN was far better than that of LR, Ridge, and Lasso. The respective RMSEs were 0.3738, 2.2831, 1.3161, and 1.0246 for all the participants; 0.3727, 1.7176, 1.3914, and 1.3524 for those with trauma; and 0.3728, 1.7516, 1.1012, and 0.8889 for those without trauma. In terms of RNN variable importance, lower-extremity motor strength (right and left ankle dorsiflexors, right knee extensors, and left long toe extensors) and the neurological level of injury were ranked among the top five across the boards. Therefore, initial FAC was the seventh, third, and ninth most important predictor for all participants, those with trauma, and those without trauma, respectively. In conclusion, this study developed a deep learning-based prediction model with excellent performance for gait recovery after SCI at the time of discharge from an acute rehabilitation facility. This study also demonstrated the strength of deep learning as an explainable artificial intelligence method for identifying the most important predictors.

Effective Deep Learning based Prediction Model for Groundwater Quality Assessment using Physio-chemical Parameters

<p>The purpose of this study is to anticipate and investigate the groundwater quality in the regions of the Cauvery basin. In the post-monsoon season of 2020, 800 samples were collected from 200 different places for this paper. The AlexNet model is used in this book to forecast the water quality. In order to examine the groundwater quality, the anticipated groundwater samples are analyzed using main cations (K+, Mg2+, Ca2+, and Na+), major anions (NO2- +NO3-, CO3-, F-, HCO3-, Cl-, and SO42-), hardness, pH, and Electrical Conductivity (EC). The accuracy, specificity, and sensitivity of the AlexNet model are used in this publication to validate its efficacy. Validating the following metrics determines if water is suitable for drinking and irrigation: sodium percentage, magnesium ratio, Kelly's ratio, sodium adsorption ratio, water quality index (WQI), and residual sodium carbonate (RSC). According to the research, the AlexNet model predicted water quality with 93%, 94.57%, and 91.47% accuracy, specificity, and sensitivity, respectively. According to the experimental results, the WQI value shows that 22% of the collected samples are acceptable for drinking, 63% are suitable for drinking, and the other 15% are only fit for irrigation and should not be consumed.</p>

Privacy-Enhanced Heart Disease Prediction in Cloud-Based Healthcare Systems: A Deep Learning Approach with Blockchain-Based Transmission

The increasing adoption of cloud computing in healthcare presents immense opportunities for disease prediction, while raising critical privacy concerns. This study proposes a novel privacy-preserving scheme that leverages advanced cryptographic techniques, blockchain technology and deep learning approach within a cloud platform, to ensure secure data handling and accurate disease prediction. The proposed methodology encompasses authentication, encryption, blockchain-based transmission, and a deep learning-based heart disease prediction system (HDPS). Through rigorous authentication protocols and two-level security mechanisms, patient data is securely encrypted using RSA and Blowfish encryption before storage in the cloud. Blockchain technology facilitates secure data transmission, ensuring integrity and traceability. At the receiver end, data decryption precedes input into the HDPS, comprising artificial neural networks (ANN), convolutional neural networks (CNN), and recurrent neural networks (RNN). The HDPS incorporates data preprocessing, feature extraction, feature selection, and a deep learning-based prediction model, achieving remarkable accuracy (0.9941) in heart disease prediction. Implemented in MATLAB, this approach offers a robust framework for privacy-preserving heart disease prediction in cloud-based healthcare systems.

Development and Validation of Deep Learning Model for Intermediate-Stage Hepatocellular Carcinoma Survival with Transarterial Chemoembolization (MC-hccAI 002): a Retrospective, Multicenter, Cohort Study.

Background: There are few effective prediction models for intermediate-stage hepatocellular carcinoma (IM-HCC) patients treated with transarterial chemoembolization (TACE) to predict overall survival (OS) is available. The learning survival neural network (DeepSurv) was developed to showed a better performance than cox proportional hazards model in prediction of OS. This study aimed to develop a deep learning-based prediction model to predict individual OS. Methods: This multicenter, retrospective, cohort study examined data from the electronic medical record system of four hospitals in China between January 1, 2007, to December 31, 2016. Patients were divided into a training set(n=1075) and a test set(n=269) at a ratio of 8:2 to develop a deep learning-based algorithm (deepHAP IV). The deepHAP IV model was externally validated on an independent cohort(n=414) from the other three centers. The concordance index, the area under the receiver operator characteristic curves, and the calibration curve were used to assess the performance of the models. Results: The deepHAP IV model had a c-index of 0.74, whereas AUROC for predicting survival outcomes of 1-, 3-, and 5-year reached 0.80, 0.76, and 0.74 in the training set. Calibration graphs showed good consistency between the actual and predicted OS in the training set and the validation cohort. Compared to the other five Cox proportional-hazards models, the model this study conducted had a better performance. Patients were finally classified into three groups by X-tile plots with predicted 3-year OS rate (low: ≤ 0.11; middle: > 0.11 and ≤ 0.35; high: >0.35). Conclusion: The deepHAP IV model can effectively predict the OS of patients with IM-HCC, showing a better performance than previous Cox proportional hazards models.

Time-history performance optimization of flapping wing motion using a deep learning based prediction model

Flapping Wing Micro Aerial Vehicles (FWMAVs) have caused great concern in various fields because of their high efficiency and maneuverability. Flapping wing motion is a very important factor that affects the performance of the aircraft, and previous works have always focused on the time-averaged performance optimization. However, the time-history performance is equally important in the design of motion mechanism and flight control system. In this paper, a time-history performance optimization framework based on deep learning and multi-island genetic algorithm is presented, which is designed in order to obtain the optimal two-dimensional flapping wing motion. Firstly, the training dataset for deep learning neural network is constructed based on a validated computational fluid dynamics method. The aerodynamic surrogate model for flapping wing is obtained after the convergence of training. The surrogate model is tested and proved to be able to accurately and quickly predict the time-history curves of lift, thrust and moment. Secondly, the optimization framework is used to optimize the flapping wing motion in two specific cases, in which the optimized propulsive efficiencies have been improved by over 40% compared with the baselines. Thirdly, a dimensionless parameter Cvariation is proposed to describe the variation of the time-history characteristics, and it is found that Cvariation of lift varies significantly even under close time-averaged performances. Considering the importance of time-history performance in practical applications, the optimization that integrates the propulsion efficiency as well as Cvariation is carried out. The final optimal flapping wing motion balances good time-averaged and time-history performance.

PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features

Protein–peptide interactions play a crucial role in various cellular processes and are implicated in abnormal cellular behaviors leading to diseases such as cancer. Therefore, understanding these interactions is vital for both functional genomics and drug discovery efforts. Despite a significant increase in the availability of protein–peptide complexes, experimental methods for studying these interactions remain laborious, time-consuming, and expensive. Computational methods offer a complementary approach but often fall short in terms of prediction accuracy. To address these challenges, we introduce PepCNN, a deep learning-based prediction model that incorporates structural and sequence-based information from primary protein sequences. By utilizing a combination of half-sphere exposure, position specific scoring matrices from multiple-sequence alignment tool, and embedding from a pre-trained protein language model, PepCNN outperforms state-of-the-art methods in terms of specificity, precision, and AUC. The PepCNN software and datasets are publicly available at https://github.com/abelavit/PepCNN.git.

Composite propeller design optimization for cavitation minimization using deep learning-based objective parameter prediction model

Recently, composite propellers have attracted attention as a means of reducing cavitation. To maximize cavitation reduction when using a composite propeller, the design of a composite propeller must be optimized. In this study, deep learning-based prediction models for composite propeller design optimization and design optimization procedures based on these models are proposed. The prediction models are trained using a training dataset consisting of the training input data obtained from a data scan grid, and the training output data include cavitation volume, adaptive deformation, and failure index. To minimize cavitation on a composite propeller, a composite propeller design optimization procedure for propeller geometry and composite lay-up sequence based on the developed prediction models is established. By following the proposed procedure, an optimized composite propeller design that minimizes cavitation volume and adaptive deformation is obtained. The cavitation of the optimized composite propeller is approximately half that of the original propeller. This result verifies the effectiveness of the proposed design optimization procedure based on the developed prediction models.

Clinical Interpretability of Deep Learning for Predicting Microvascular Invasion in Hepatocellular Carcinoma by Using Attention Mechanism.

Preoperative prediction of microvascular invasion (MVI) is essential for management decision in hepatocellular carcinoma (HCC). Deep learning-based prediction models of MVI are numerous but lack clinical interpretation due to their "black-box" nature. Consequently, we aimed to use an attention-guided feature fusion network, including intra- and inter-attention modules, to solve this problem. This retrospective study recruited 210 HCC patients who underwent gadoxetate-enhanced MRI examination before surgery. The MRIs on pre-contrast, arterial, portal, and hepatobiliary phases (hepatobiliary phase: HBP) were used to develop single-phase and multi-phase models. Attention weights provided by attention modules were used to obtain visual explanations of predictive decisions. The four-phase fusion model achieved the highest area under the curve (AUC) of 0.92 (95% CI: 0.84-1.00), and the other models proposed AUCs of 0.75-0.91. Attention heatmaps of collaborative-attention layers revealed that tumor margins in all phases and peritumoral areas in the arterial phase and HBP were salient regions for MVI prediction. Heatmaps of weights in fully connected layers showed that the HBP contributed the most to MVI prediction. Our study firstly implemented self-attention and collaborative-attention to reveal the relationship between deep features and MVI, improving the clinical interpretation of prediction models. The clinical interpretability offers radiologists and clinicians more confidence to apply deep learning models in clinical practice, helping HCC patients formulate personalized therapies.

Diabetic retinopathy stage detection using convolutional fine-tuned transfer Learning model

Diabetic Retinopathy (DR) is a prevalent eye condition that occurs as a frequent complication among individuals with diabetes, particularly those who have been living with the disease for an extended period of time. This study uses fundus images to diagnose DR at five stages from early to late with No DR, Mild, Moderate, Severe, and Proliferative DR, commonly known as Stage 0 to Stage 4, respectively. This will aid in the timely treatment of diabetic patients preventing them from developing DR as early as possible. We used two most popular open-source datasets, the DR Detection database, namely APTOS 2019 and EyePACS, and combined them to create a larger dataset to trade off the data-centric obstacle and shortfall for any Deep Learning-based prediction models. Data augmentation and preprocessing techniques are applied to the images before feeding them to the proposed model to get a more accurate and efficient one. In the modern age oriented to Artificial Intelligence (AI), it is necessary to thoroughly analyze the identification of DR based on the existing Deep Learning (DL) models. After learning about the limitations of existing models, we have fine-tuned the ResNet50, DenseNet201 and InceptionV3 to enhance the model performance of the detection and categorization of DR. We have since proposed three Deep Convolutional Neural Networks (DCNN) models with better outcome based on accuracy than the existing state-of-the-art (SOTA) models. The fine-tuned DenseNet201 model, among the other two, performed significantly better with a validation accuracy of 90.04% and a negligible amount of loss, irrespective of each class, under the best configurable test conditions.

Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images

BackgroundGastrointestinal (GI) infections are quite common today around the world. Colonoscopy or wireless capsule endoscopy (WCE) are noninvasive methods for examining the whole GI tract for abnormalities. Nevertheless, it requires a great deal of time and effort for doctors to visualize a large number of images, and diagnosis is prone to human error. As a result, developing automated artificial intelligence (AI) based GI disease diagnosis methods is a crucial and emerging research area. AI-based prediction models may lead to improvements in the early diagnosis of gastrointestinal disorders, assessing severity, and healthcare systems for the benefit of patients as well as clinicians. The focus of this research is on the early diagnosis of gastrointestinal diseases using a convolution neural network (CNN) to enhance diagnosis accuracy. MethodsVarious CNN models (baseline model and using transfer learning (VGG16, InceptionV3, and ResNet50)) were trained on a benchmark image dataset, KVASIR, containing images from inside the GI tract using n-fold cross-validation. The dataset comprises images of three disease states—polyps, ulcerative colitis, and esophagitis—as well as images of the healthy colon. Data augmentation strategies together with statistical measures were used to improve and evaluate the model's performance. Additionally, the test set comprising 1200 images was used to evaluate the model's accuracy and robustness. ResultsThe CNN model using the weights of the ResNet50 pre-trained model achieved the highest average accuracy of approximately 99.80% on the training set (100% precision and approximately 99% recall) and accuracies of 99.50% and 99.16% on the validation and additional test set, respectively, while diagnosing GI diseases. When compared to other existing systems, the proposed ResNet50 model outperforms them all. ConclusionThe findings of this study indicate that AI-based prediction models using CNNs, specifically ResNet50, can improve diagnostic accuracy for detecting gastrointestinal polyps, ulcerative colitis, and esophagitis. The prediction model is available at https://github.com/anjus02/GI-disease-classification.git.

Deep Learning for Identifying Promising Drug Candidates in Drug-Phospholipid Complexes.

Drug-phospholipid complexing is a promising formulation technology for improving the low bioavailability of active pharmaceutical ingredients (APIs). However, identifying whether phospholipid and candidate drug can form a complex through in vitro tests can be costly and time-consuming due to the physicochemical properties and experimental environment. In a previous study, the authors developed seven machine learning models to predict drug-phospholipid complex formation, and the lightGBM model demonstrated the best performance. However, the previous study was unable to sufficiently address the degradation of test performance caused by the small size of the training data with class imbalance, and it had the limitation of considering only machine learning techniques. To overcome these limitations, we propose a new deep learning-based prediction model that employs variational autoencoder (VAE) and principal component analysis (PCA) techniques to improve prediction performance. The model uses a multi-layer one-dimensional convolutional neural network (CNN) with a skip connection to effectively capture the complex relationship between drugs and lipid molecules. The computer simulation results demonstrate that our proposed model performs better than the previous model in all performance metrics.

Revisiting Electrocatalyst Design by a Knowledge Graph of Cu-Based Catalysts for CO2 Reduction

Electrocatalysis takes a significant role in the production of sustainable fuels and chemicals. The combination of artificial intelligence and catalytic science is exhibiting great potential to extract, analyze, and predict electrocatalysts. However, the currently developed machine learning approach usually requires a mass of data from density functional theory calculations to train and optimize models. In contrast, a knowledge graph has the potential to extract useful information from a large amount of the literature without referring to density functional theory. Herein, a knowledge graph of Cu-based electrocatalysts for electrocatalytic CO2 reduction is constructed based on a linguistically enriched SciBERT-based framework. This framework retrieves multiple types of entities including material, regulation method, product, Faradaic efficiency, etc. from 757 scientific literature, generates representations with abundant domain-specific semantic information, and exhibits the capability to deal with electrocatalysts for CO2 reduction. The obtained graph shows the development history of related catalysts, builds relationships between the factors associated with catalysis, and provides intuitive charts for researchers to gain useful information. Furthermore, we propose a deep learning-based prediction model, which integrates the semantic information from the scientific literature (word embedding) with the correlation of knowledge triples (graph embedding) and realizes the prediction of the Faradaic efficiency for a targeted case. This work paves the way for catalyst design in the manner of merging artificial intelligence with catalytic science.

Deep learning-based prediction model for predicting the tumor origin of cancers of unknown primary.

e13562 Background: Identifying the origin of tumors is important for the treatment and prognosis of cancer patients. Despite of routine examinations including medical history, laboratory tests, histopathological assessments and imaging, 3-5% of metastatic cancers are diagnosed with cancers of unknown primary (CUP). In clinical practice, the management of CUP is challenging and empirical chemotherapy remains to be the main treatment option, leading to poor prognosis. Methods: To predict the tumor origin, we built a weighted ensemble model by Automated machine learning (autoML), including 13 models, using molecular and clinical datasets consisted of 22,018 patients and 28tumor types. The tumor type of each patient was confirmed by at least 3 pathologists. Our molecular and clinical datasets contained 7 features including age, tumor site, gene alteration, tumor mutation burden (TMB), microsatellite instability (MSI), etc. Features selected by AutoML were used to train the diagnostic model by using algorithms including CatBoost, ExtraTreesEntr (Extremely randomized trees), ExtraTreesGini, KNeighborsDist, KNeighborsUnif, LightGBM, XGBoost, etc. The best model was selected by autoML. We then optimized the model parameters and developed a No-Bias algorithm to ensure that the trained model can output "fair and unbiased" prediction results even when the input dataset had an uneven distribution in the number of cases for each cancer type. 5-fold cross validation was used to estimate the accuracy of the diagnostic models. Moreover, we validated the accuracy of diagnostic model using an independent validation set of 1,636 patients covering 28 tumor types. Results: Our model outputs the most confidently predicted tumor types in the form of TOP N (N=1-5). In the training set, the accuracy of TOP1, TOP3, and TOP5 of our diagnostic model was 72.8%, 88.4%, and 93.3%, respectively. had been successfully applied to support the diagnosis of 10 CUP patients. In one patient who was predicted to have esophageal squamous cell carcinoma, immune checkpoint inhibitor combined with chemotherapy regimen was given and achieved a 9-mouths PFS. Conclusions: We successfully developed a deep learning-based diagnostic model for predicting tumor origin with genomic and clinical data. The model could be applied to improve the clinical diagnosis of CUP patients and guide the treatment decision according to the predicted tumor origin.

Predictive Modeling of Out-of-Plane Deviation for the Quality Improvement of Additive Manufacturing

Additive manufacturing (AM) is a new technology for fabricating products straight from a 3D digital model, which can lower costs, minimize waste, and increase building speed while maintaining acceptable quality. However, it still suffers from low dimensional accuracy and a lack of geometrical quality standards. Moreover, there is a need for a robust AM configuration to perform in-situ inspections during the fabrication. This work established a 3D printing-scanning setup to collect 3D point cloud data of printed parts and then compare them with nominal 3D point cloud data to quantify the deviation in all X, Y, and Z directions. Specifically, this work aims at predicting the anticipated deviation along the Z direction by applying a deep learning-based prediction model. An experiment with regard to a human “Knee” prototype fabricated by Fused Deposition Modeling (FDM) is conducted to show the effectiveness of the proposed methods.

Cryptocurrency Price Prediction using Machine Learning

Crypto currency has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Crypto currency price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a Machine Learning (ML), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Crypto currency price prediction. Crypto currency is one of the most popular and valuable crypto currency in the current financial market, attracting traders for investment and thereby opening new research opportunities for researchers. Countless research works have been performed on Crypto currency price prediction with different machine learning prediction algorithms. For the research: relevant features are taken from the dataset having strong correlation with Crypto currency prices and random data chunks are then selected to train and test the model. The random data which has been selected for model training, may cause unfitting outcomes thus reducing the price prediction accuracy. Here, a proper method to train a prediction model is being scrutinised. The proposed methodology is then applied to train a simple Long Short-Term Memory (LSTM) model to predict the crypto currency price for the upcoming 5 days. When the LSTM model is trained with a suitable data chunk, thus identified, sustainable results are found for the prediction. In the end of this paper, the work culminates with future improvements. In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable. Connecting Django server to create front end using HTML, CSS, Java Script