Validation Accuracy Research Articles

Unveiling the impact of cell death-related genes and immune dynamics on drug resistance in lung adenocarcinoma: a risk score model and functional insights

Lung adenocarcinoma (LUAD), characterized by its heterogeneity and complex pathogenesis, is the focus of this study which investigates the association between cell death-related genes and LUAD. Through machine learning, a risk score model was developed using the Coxboost rsf algorithm, demonstrating strong prognostic accuracy in both validation (GSE30219, GSE31210, GSE72094) and training (TCGA-LUAD) datasets with C-indices of 0.93, 0.67, 0.68, and 0.64, respectively. The study reveals that the expression of Keratin 18 (KRT18), a key cytoskeletal protein, varies across LUAD cell lines (DV-90, PC-9, A549) compared to normal bronchial epithelial cells (BEAS-2B), suggesting its potential role in LUAD's pathogenesis. Kaplan–Meier survival curves further validate the model, indicating longer survival in the low-risk group. A comprehensive analysis of gene expression, functional differences, immune infiltration, and mutations underscores significant variations between risk groups, highlighting the high-risk group's immunological dysfunction. This points to a more intricate tumor immune environment and the possibility of alternative therapeutic strategies. The study also delves into drug sensitivity, showing distinct responses between risk groups, underscoring the importance of risk stratification in treatment decisions for LUAD patients. Additionally, it explores KRT18's epigenetic regulation and its correlation with immune cell infiltration and immune regulatory molecules, suggesting KRT18's significant role in the tumor immune landscape. This research not only offers a valuable prognostic tool for LUAD but also illuminates the complex interplay between cell death-related genes, drug sensitivity, and immune infiltration, positioning KRT18 as a potential therapeutic or prognostic target to improve patient outcomes by personalizing LUAD treatment strategies.

A novel approach to crack modeling using extended finite element and substructures methods

In the present work, a combination of Extended Finite Element Method (XFEM) and substructures methods has been utilized for crack modeling. Structures are divided into several substructures, and XFEM is employed to model the presence of cracks within each substructure. In another method, crack modeling has been carried out without using the Extended Finite Element Method. Instead, cracks are modeled by eliminating boundary constraints from two substructures. The Krylov-Schur algorithm is utilized to accelerate the computation of structural modes by preparing stiffness and mass matrices for eigenvalue and eigenvector calculations related to the superstructure. For evaluating this method, four examples have been considered: In the first example, an experimental L-shaped steel structure comprising a beam and a column was examined. The second example involved an experimental steel beam with a box-shaped cross-section. In the third example, a steel beam with simple supports at both ends and a rectangular cross-section was proposed. In the fourth example, a real two-span bridge (Turtle Mountain Bridge) is also presented to evaluate the proposed method. The method was applied for modeling, and the obtained results were validated using experimental and analytical results. In this work, the Structural Similarity Index Method (SSIM) for mode shape similarity assessment has been employed for crack identification. Based on the results obtained from the modeling and crack identification evaluations, it can be concluded that the current method can model multiple cracks in structures with variable cross-sections, real concrete bridges, frame-shaped structures, and diverse structures by assembling substructures. Despite simplifying the problems, it maintains an acceptable level of accuracy in validation. This advantage enables the identification of cracks through a straightforward comparison of mode shapes.

Psychometric validation of the Italian Self-Report Symptoms inventory (SRSI): Factor structure, construct validity, and diagnostic accuracy

The Self-Report Symptom Inventory (SRSI) is a novel tool designed to detect symptom overreporting and other forms of noncredible responding. Unlike existing scales, the SRSI includes genuine and pseudosymptoms scales covering cognitive, affective, motor, pain, and post-traumatic stress disorder domains. The present study aims to investigate the psychometric properties of the Italian Version of the SRSI (SRSI-It), in particular, its factor structure, reliability, convergent and discriminant validity, and diagnostic accuracy. Data from 1180 healthy participants showed a hierarchical structure with higher-order constructs for genuine symptoms and pseudosymptoms, each comprising five subscales. The SRSI-It showed a strong convergent validity with the Structured Inventory of Malingered Symptomatology and discriminant validity through low correlations with the Psychopathic Personality Inventory-Revised. Receiver operating characteristic analysis determined cut scores of 6 (95% specificity) and 9 (98% specificity) for pseudosymptoms, with a Ratio Index score of 0.289 (82% specificity). In summary, the SRSI-It appears to be a promising tool for identifying symptom exaggeration in clinical and forensic contexts, ultimately enhancing the quality and reliability of evaluations in these contexts.

A ResNet based transfer learning pulmonary medical image disease detection system

Medical imaging represents a significant application of deep learning. In response to the vast domain of medical image analysis and the limitations and inefficiencies of conventional methods for diagnosing brain diseases, this paper explores the application of an enhanced ResNet-50 model in the classification of brain CT images. The goal is to improve the detection and classification accuracy of aneurysms, cancer, and malignant tumors. The study utilized 259 images, undergoing training, validation, and testing processes to verify the model's efficacy. The ResNet-50 model addresses the issue of vanishing gradients in deep networks through residual learning, making it suitable for high-resolution images and small datasets. Results indicate good training performance, though some fluctuations in validation performance, achieving a validation accuracy of 0.960. This research not only enhances diagnostic accuracy for brain diseases but also paves a new path for the further application of deep learning technologies in the medical field.

Deep Learning-Based Artificial Intelligence Can Differentiate Treatment-Resistant and Responsive Depression Cases with High Accuracy.

Background: Although there are many treatment options available for depression, a large portion of patients with depression are diagnosed with treatment-resistant depression (TRD), which is characterized by an inadequate response to antidepressant treatment. Identifying the TRD population is crucial in terms of saving time and resources in depression treatment. Recently several studies employed various methods on EEG datasets for automatic depression detection or treatment outcome prediction. However, no previous study has used the deep learning (DL) approach and EEG signals for detecting treatment resistance. Method: 77 patients with TRD, 43 patients with non-TRD, and 40 healthy controls were compared using GoogleNet convolutional neural network and DL on EEG data. Additionally, Class Activation Maps (CAMs) acquired from the TRD and non-TRD groups were used to obtain distinctive regions for classification. Results: GoogleNet classified the healthy controls and non-TRD group with 88.43%, the healthy controls and TRD subjects with 89.73%, and the TRD and non-TRD group with 90.05% accuracy. The external validation accuracy for the TRD-non-TRD classification was 73.33%. Finally, the CAM analysis revealed that the TRD group contained dominant features in class detection of deep learning architecture in almost all electrodes. Limitations: Our study is limited by the moderate sample size of clinical groups and the retrospective nature of the study. Conclusion: These findings suggest that EEG-based deep learning can be used to classify treatment resistance in depression and may in the future prove to be a useful tool in psychiatry practice to identify patients who need more vigorous intervention.

Assessment of three Artificial Intelligence models performance in the detection of bacterial spot on tomato leaves in mozambique

The agricultural sector is pivotal to Mozambique's economy through domestic consumption and exports of crops such as (Lycopersicon esculentum Mill.). However, tomato production faces several challenges, one of the most important being the bacterial diseases infestation. Artificial Intelligence (AI) has emerged as a transformative tool across industries, including agriculture, leveraging machine learning and extensive datasets to enhance productivity and sustainability. In a study conducted at Eduardo Mondlane University experimental trial station in Maputo, images of tomato leaves were collected and categorized to develop AI models for detection of bacterial spot disease. Data preprocessing included resizing and augmentation techniques to optimize model performance. Training involved Convolutional Neural Networks (CNNs) using TensorFlow and Adam optimizers. Among models tested (Resnet 50, Inception V3, VGG 16). The VGG 16 with data augmentation achieved the highest validation accuracy of 93.33% and minimal loss (0.1595%), demonstrating superior precision and generalization. This underscores the critical role of data augmentation in training robust AI models for effective Bacterial Spot detection in tomatoes, offering significant potential for improving agricultural outcomes in Mozambique.

Attention-Based Light Weight Deep Learning Models for Early Potato Disease Detection

Potato crop has become integral part of our diet due to its wide use in variety of dishes, making it an important food crop. Its importance also stems from the fact that it is one of the cheapest vegetables available throughout the year. This makes it crucial to keep potato prices affordable for developing countries where the majority of the population falls under the middle-income bracket. Consequently, there is a need to develop a robust, effective, and portable technique to detect diseases in potato plant leaves. In this work, an attention-based disease detection technique is proposed. This technique selectively focuses on specific parts of an image which reveal the disease. This technique leverages transfer learning combined with two attention modules: the channel attention module and spatial attention module. By focusing on specific parts of the images, the proposed technique is able to achieve almost similar accuracy with significantly fewer parameters. The proposed technique has been validated using four pre-trained models: DenseNet169, XceptionNet, MobileNet, and VGG16. All of these models are able to achieve almost the same level of training and validation accuracy, around 90–97%, even after reducing the number of parameters by 40–50%. It shows that the proposed technique effectively reduces model complexity without compromising performance.

Machine learning with analysis-of-variance-based method for identifying rice varieties

Rice, which has the highest production and consumption rates worldwide, is one of the main nutrients in our country because it is economical and nutritious. Rice undergoes various stages of production from the field to the dinner tables, with the cleaning phase involving separation of rice from unwanted materials. There are different varieties of rice such as arborio, basmati, aracadaga, ipsala, and jasmine. Therefore, it is necessary to identify rice varieties based on customer demands. Rice variety identification is primarily based on its outer appearance, including color, size, and shape. In this study, 75,000 rice grain images, including 15,000 images for each variety, were considered. Using a combination of 12 morphological features, four shape features, and 90 color features obtained from five different color spaces, 106 features were extracted from the images. An analysis of variance (ANOVA) was employed to select high-rank features, which were then fed to a support vector machine (SVM) for classification. The experimental results demonstrated that the utilization of 40 high-rank features yielded impressive outcomes, with a validation accuracy of 99.89 %, cost value of 16, test accuracy of 99.81 %, and cost value of 28. These high accuracy rates are particularly noteworthy, as they indicate the robustness and reliability of the proposed methodology in distinguishing between different rice varieties with minimal error. This high level of precision is critical for practical applications, such as quality control in rice production, to ensure that customers receive the specific rice variety they demand. Implementing this methodology in real-world scenarios could significantly enhance the efficiency and accuracy of the rice variety identification processes, benefiting both producers and consumers.

Optimization of machine learning model training procedure on multi-gpu systems to enhance cyber security in telecommunication networks

This paper analyzes the optimization features of machine learning (ML) model training procedures using multi-GPU systems to enhance cyber security in telecommunication networks. A key aspect of the study is the use of data parallelism, which allows the distribution of the training load across multiple GPUs, significantly reducing training time and improving model accuracy-critical factors for rapid threat detection in cyberspace. A novel approach for optimizing data batch size using Mutual Information (MI) is proposed, which harmonizes the utilization of computational resources with the information content of the training data. MI helps to determine the optimal data batch size that minimizes training errors and improves model accuracy without a significant increase in training time. Experimental results demonstrate the substantial advantages of multi-GPU configurations compared to single-GPU setups, providing faster training and improved model accuracy. It was particularly emphasized that MI-guided batch size tuning significantly outperforms traditional manual tuning methods, ensuring higher validation accuracy and reducing training time. The study showed that the MI-based approach is an effective tool for addressing the problem of optimizing ML model training processes in real-world scenarios where cyber security is critical. The proposed methods allow ML models to train faster and more accurately identify potential threats, making them particularly relevant for telecommunication networks where a rapid response to new threats in real time is required. The implementation of modern computational technologies such as multi-GPU systems and MI-optimized training enhances the efficiency and accuracy of machine learning models. This, in turn, improves cyber security measures and ensures a more reliable defence of telecommunication networks against malicious attacks. It is noted that the proposed approaches can be adapted not only for cyber security but also for other areas where high model accuracy and fast training are important. Future research prospects include the development of new machine learning methods, particularly deep neural networks, the exploration of alternative computational architectures such as quantum computing or distributed systems, and their integration into real-time systems. Special attention should be paid to the ethical aspects of implementing automated cyber security systems, particularly in preventing bias in algorithms and ensuring fairness in their application.

Classification and detection of noise in IoT based MQ gas sensors using artificial neural network

Abstract IoT-based Metal Oxide Semiconductor (MOS) gas sensors have potential applications in health, industrial, and agriculture sectors. MQ gas sensors are easy to use, have a large detection range, high sensitivity, and efficiency, and can be interfaced with Arduino for effective use. However, like other any other sensors, the MQ gas sensors are also not able to escape the effect of noise that affects the sensitivity and selectivity causing the unwanted wrong classification and identification of gases, etc finally leading to a wrong justification of results and data. Hence, the study of noise and its removal is very crucial for greater accuracy in its analysis. The main aim of our work is to check whether one can classify different types of noises or noisy signals accurately using even simple ANNs so that after testing the different filtration techniques for the signals we can obtain an independent system specifically for the MQ gas sensors, that can classify as well as filter the noisy signals based on the category to which they are classified. The best and most effective noise filtration method is then obtained. During the study Narrow Neural Network model and Medium Neural Network demonstrated high accuracy in validation and testing, with ROC curves indicating their efficiency and effectiveness.

Brain white matter pathways of resilience to chronic back pain: a multisite validation.

Chronic back pain (CBP) is a global health concern with significant societal and economic burden. While various predictors of back pain chronicity have been proposed, including demographic and psychosocial factors, neuroimaging studies have pointed to brain characteristics as predictors of CBP. However, large-scale, multisite validation of these predictors is currently lacking. In two independent longitudinal studies, we examined white matter diffusion imaging data and pain characteristics in patients with subacute back pain (SBP) over six- and 12-month periods. Diffusion data from individuals with CBP and healthy controls (HC) were analyzed for comparison. Whole-brain tract-based spatial statistics analyses revealed that a cluster in the right superior longitudinal fasciculus (SLF) tract had larger fractional anisotropy (FA) values in patients who recovered (SBPr) compared to those with persistent pain (SBPp), and predicted changes in pain severity. The SLF FA values accurately classified patients at baseline and follow-up in a third publicly available dataset (Area under the Receiver Operating Curve ∼ 0.70). Notably, patients who recovered had FA values larger than those of HC suggesting a potential role of SLF integrity in resilience to CBP. Structural connectivity-based models also classified SBPp and SBPr patients from the three data sets (validation accuracy 67%). Our results validate the right SLF as a robust predictor of CBP development, with potential for clinical translation. Cognitive and behavioral processes dependent on the right SLF, such as proprioception and visuospatial attention, should be analyzed in subacute stages as they could prove important for back pain chronicity.

Crack detection in buildings using the YOLO v8 network

The objective of this study is to develop and apply deep neural networks for the automation of crack detection in buildings. The methodology involved training the YOLO v8 network with images collected from the internet, aiming to identify and locate cracks in real time. The model obtained 80% accuracy in validation with images not used in training, despite performance limitations in Google Collab. These limitations included restrictions on the execution environment, and the model is specific to cracks. The originality of the tool lies in its relevance for the automated detection of cracks, with the potential to extend to other pathological manifestations. It is concluded that the application of deep neural networks offers an efficient solution for the identification of problems in buildings.

Evaluating the performance of a non-uniform squash function in Capsule networks for early diabetic retinopathy detection using fundus image analysis

Diabetic retinopathy, known as one of the most crucial neurovascular barriers to diabetes, has affected the retina, leading to a potential risk of blindness. The harm is not isolated to personal health, with negative effects felt at the economic and social level in entire communities. The consequences of vision loss secondary to diabetic retinopathy on quality of life, independent living, and employment can be significant. It is necessary to adopt a comprehensive approach for effective management of underlying diabetes, which includes early detection, timely intervention, supportive care, and management of comorbid conditions. Critical to this is a precise classification of its stages for disease analysis. A manual diagnosis, on the other hand, requires qualified individuals to recognize the subtle nuances of the condition, making it a lengthy process. This study aims to develop a computer-assisted system for analyzing retinal fundus images to identify and classify different stages of diabetic retinopathy. A dataset of eye fundus images was used by the proposed network to classify the presence and severity of blindness and its stages. The proposed model uses a non-uniform squash function in capsule neural networks. The motive behind employing a non-uniform squash function was to encourage the model to allocate greater attention and stability to intricate images throughout the training phase. The proposed model's efficiency is assessed meticulously with pre-trained CNN methods like InceptionV3, VGG16, VGG19, and Xception. The proposed capsule network model has been revamped to address the shortcomings of prior research with care. The method stands out with an impressive 99.84 % training accuracy and 98.68 % validation accuracy. Diabetic retinopathy, though rare in occurrence, poses significant challenges due to the high cost of diagnosis, rendering it inaccessible to rural and remote communities with limited access to tests and check-ups.

Identifying long-term burned forests in the rugged terrain of Southwest China:A novel method based on remote sensing and ecological mechanisms

Burned forests were detected using remote sensing techniques. Yet, identifying long-term burned forests in the mountains, especially at a large spatial extent, remains a great challenge due to a lack of long-term high-resolution remote sensing data or the unsatisfactory performance of the moderate-resolution remotely sensed data in complex mountain landscapes. Efficient, robust, and cost-effective methods are urgently called for. In this study, we developed a novel method combining remote sensing and ecological mechanisms (short for RSEM-M) with a paired-pixel approach to identify the long-term burned forests in Southwest China, where the terrain is rugged. In mapping burned forests in 2010, the overall accuracy, producer accuracy, user accuracy, Kappa coefficient, and field validation accuracy were 92.27 %, 95.86 %, 88.36 %, 0.85, and 81.16 %, respectively. Compared to the standard procedure and FireCCI51 and MCD64A1 fire products, the RSEM-M shows higher accuracy and consistency in identifying burned forests of different sizes, with fewer invalid pixels, better spatial continuity, and more accurate boundary delineation. Then, using the RSEM-M, we mapped burned forests in Southwest China from 2002 to 2017. The cumulative areas were 37.69 × 104 ha. The RSEM-M improved the efficiency and accuracy of long-term burned forest identifications in mountainous landscapes at a large spatial extent.

Enhancing Paddy Leaf Disease Classification using CNN and MobileNetV2

Paddy farming, a cornerstone of global agriculture, faces significant threats from various diseases that affect the crop yield. This research presents a novel approach for detecting paddy leaf diseases using advanced deep learning techniques, specifically transfer learning with the MobileNetV2 architecture. The methodology involves the utilization of a comprehensive dataset consisting of paddy leaf images across multiple disease classes. Data augmentation was extensively employed to address the limitations posed by the dataset size. Both basic and advanced models were trained, with the advanced model achieving a remarkable validation accuracy of 97%. Additionally, Time-Test Augmentation (TTA) was applied to further enhance the model's performance. This research demonstrates the efficacy of deep learning techniques in agricultural disease detection and highlights potential improvements for future applications.

Garbage Image Classifier using Modified ResNet-50

This research proposes a deep learning model pretrained with ResNet-50 to classify 12 types of garbage. The model uses a modified ResNet-50 architecture with the Adamax and Adadelta optimizers and varying learning rates (0.1, 0.01, and 0.001). Six experiments were conducted to determine the most optimal training parameter configuration for the proposed model. Results show that the model performed best with the Adadelta optimizer and a learning rate of 0.1, achieving a validation accuracy of 93.85%. In comparison, the Adamax optimizer with a learning rate of 0.001 yielded a validation accuracy of 93.44%. Despite these results, there is a tendency for misclassification in the metal, plastic, and white-glass classes. Future work should focus on addressing these misclassification issues by expanding the dataset for these problematic classes. This can be achieved either by collecting additional images specific to these classes or by employing advanced data augmentation techniques to enhance the existing dataset and improve the model's accuracy.

Prediction of teicoplanin plasma concentration in critically ill patients: a combination of machine learning and population pharmacokinetics.

Teicoplanin has been widely used in patients with infections caused by Staphylococcus aureus, especially for critically ill patients. The pharmacokinetics (PK) of teicoplanin vary between individuals and within the same individual. We aim to establish a prediction model via a combination of machine learning and population PK (PPK) to support personalized medication decisions for critically ill patients. A retrospective study was performed incorporating 33 variables, including PPK parameters (clearance and volume of distribution). Multiple algorithms and Shapley additive explanations were employed for feature selection of variables to determine the strongest driving factors. The performance of each algorithm with PPK parameters was superior to that without PPK parameters. The composition of support vector regression, categorical boosting and a backpropagation neural network (7:2:1) with the highest R2 (0.809) was determined as the final ensemble model. The model included 15 variables after feature selection, of which the predictive performance was superior to that of models considering all variables or using only PPK. The R2, mean absolute error, mean squared error, absolute accuracy (±5 mg/L) and relative accuracy (±30%) of external validation were 0.649, 3.913, 28.347, 76.12% and 76.12%, respectively. Our study offers a non-invasive, fast and cost-effective prediction model of teicoplanin plasma concentration in critically ill patients. The model serves as a fundamental tool for clinicians to determine the effective plasma concentration range of teicoplanin and formulate individualized dosing regimens accordingly.

Precision Medicine for Apical Lesions and Peri-Endo Combined Lesions Based on Transfer Learning Using Periapical Radiographs.

An apical lesion is caused by bacteria invading the tooth apex through caries. Periodontal disease is caused by plaque accumulation. Peri-endo combined lesions include both diseases and significantly affect dental prognosis. The lack of clear symptoms in the early stages of onset makes diagnosis challenging, and delayed treatment can lead to the spread of symptoms. Early infection detection is crucial for preventing complications. PAs used as the database were provided by Chang Gung Memorial Medical Center, Taoyuan, Taiwan, with permission from the Institutional Review Board (IRB): 02002030B0. The tooth apex image enhancement method is a new technology in PA detection. This image enhancement method is used with convolutional neural networks (CNN) to classify apical lesions, peri-endo combined lesions, and asymptomatic cases, and to compare with You Only Look Once-v8-Oriented Bounding Box (YOLOv8-OBB) disease detection results. The contributions lie in the utilization of database augmentation and adaptive histogram equalization on individual tooth images, achieving the highest comprehensive validation accuracy of 95.23% with the ConvNextv2 model. Furthermore, the CNN outperformed YOLOv8 in identifying apical lesions, achieving an F1-Score of 92.45%. For the classification of peri-endo combined lesions, CNN attained the highest F1-Score of 96.49%, whereas YOLOv8 scored 88.49%.

Myocardial scar area predicts major adverse cardiovascular events after coronary artery bypass grafting in patients with ischemic cardiomyopathy

Objective: To investigate the value of myocardium scar area in predicting adverse cardiovascular events (MACEs) after coronary artery bypass grafting (CABG) in patients with ischemic cardiomyopathy (ICM). Methods: The first part of this study was a retrospective study. Patients diagnosed with ICM and undergoing CABG surgery at Beijing Anzhen Hospital, Capital Medical University from January 2017 to December 2022 were enrolled as the discovery cohort. All patients underwent cardiac magnetic resonance-late gadolinium enhancement (CMR-LGE) before surgery. According to the occurrence of postoperative MACEs, the patients were divided into MACEs group and MACEs-free group. Preoperative clinical and imaging data, intraoperative and postoperative data were collected and compared between the two groups. The primary endpoint was postoperative MACEs. Univariate and multifactor regression analyses were used to analyze the risk factors for MACEs. Receiver operating characteristic (ROC) curves were constructed to evaluate the predictive efficacy and optimal cut-off value of myocardial scar area for endpoint events. The second part of this study was a prospective study. Patients with ICM who received CABG at Beijing Anzhen Hospital, Capital Medical University from January 2023 to June 2023 were enrolled as a validation cohort, and were divided into MACEs group and MACEs-free group according to whether MACEs occurred after surgery. Preoperative clinical and imaging data, intraoperative and postoperative data were collected and compared between the two groups. Verify the reliability of the cut-off value obtained by ROC curve in the validation cohort. Results: A total of 120 patients with ICM (30 patients in MACEs group and 90 patients in MACEs-free group), aged (61.6±8.7) years, including 93 males, were included in the discovery cohort. A total of 22 ICM patients (5 patients in MACEs group and 17 patients in MACEs-free group), aged (59.5±8.2) years, including 18 males, were included in the validation cohort. Multivariate Cox regression showed that myocardial scar area (HR=1.258, 95%CI 1.096-1.444, P=0.001) was an independent risk factor for the primary endpoint event. The area under ROC curve of myocardial scar area for predicting postoperative MACEs was 0.90 (95%CI 0.83-0.95), and myocardial scar area≥36.0% was the optimal cut-off value for predicting postoperative MACEs, and its sensitivity, specificity and accuracy were 96.7%, 72.2% and 78.3%, respectively. In the validation cohort, the sensitivity, specificity and accuracy of myocardial scar area in predicting postoperative MACEs in patients with ICM after CABG were 80.0%, 82.4% and 81.8%, respectively. Conclusion: Myocardial scar area is an independent risk factor for MACEs after CABG in patients with ICM, and myocardial scar area≥36.0% is the optimal cut-off value for predicting MACEs after CABG. Myocardial scar area can help to identify patients at high risk of surgery and provide a basis for risk stratification of patients.

Deep Learning model based on CNN using Keras and TensorFlow to determine real time melting point of chemical substances

Deep learning is a subset of machine learning that uses artificial neural networks inspired by human cognitive systems. Although this is a newly approach recently it became very popular and effective. In many applications deep learning become most successful approach where machine learning has been successful at certain rates. In the succession of these the proposed deep learning model is suitable for melting point detection apparatus which determine melting point of chemical substances this apparatus generally used in pharmaceutical and chemical industries. Proposed deep learning model classify images of chemical’s state (Solid or Liquid) by deep neural network (DNN) it consists of TensorFlow framework, libraries like Keras and activation function like ReLu, sigmoid, MaxPool and Flatten to determine melting point of chemical substances. The proposed model enables to TensorFlow architecture, which can determine the melting point of chemicals in real time on a single board computer. This use python as a programming language, TensorFlow framework and keras library. The input image data mainly focuses on chemical’s state, there are 2 categories of chemical’s state either solid or liquid. The Deep Neural Network (DNN) chosen as the best practice for the training process because it provides high accuracy. The results discussed in terms of the image classification accuracy in percentage. The images from two class label gets maximum accuracy is 99.72% and maximum validation accuracy is 99.37% same as liquid’s image and the average value of accuracy 84.17% or higher after certain epochs.