Perfect Accuracy Research Articles

Abstract PR013: Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers

Abstract Introduction: Colorectal cancer (CRC) is one of the most common cancers worldwide, and early detection is critical for successful treatment and improved survival rates. However, precancerous and early-stage CRC present significant diagnostic challenges due to the small size of lesions and the very low expression of tumor-specific biomarkers in the bloodstream. Current genomics-based diagnostic methods struggle to detect these lesions, making it imperative to develop more sensitive and specific approaches. Circulating extracellular vesicles (EVs) are emerging as a promising solution for early-stage CRC detection, since EVs are produced by tumor cells as well as the tumor microenviroment and host immune cells. Thus, EVs may offer the means to detect and monitor small early-stage tumors through both direct detection of tumor- associated biomarkers as well through tumor specific host response and tumor microenvironment biomarkers. Methods: To identify novel early-stage CRC specific biomarkers, we performed proteomics analysis on EVs purified from patient plasma using size exclusion chromatography and a proprietary buffer system that enhances EV and corona protein recovery. TrueDiscovery™ Data-independent acquisition (DIA) mass spectrometry (MS) analysis was conducted on 24 pre- cancer/stage 0 and 25 stage 1 CRC patients and 75 normal patient plasma samples. An in-house developed machine learning pipeline was used to identify differentially expressed proteins and model candidate multiplexes to identify those with extremely high diagnostic accuracy (> 0.99). Results: An average of ∼2,500 proteins were identified per sample and included in bioinformatics analysis. Comparative analysis between control patient EVs and either pre- cancerous/Stage 0, or Stage 1 colon cancer EVs using an in-house Machine Learning pipeline identified 336 and 493 differentially expressed proteins for each group (FDR adjusted p-value < 0.001). Of these, the best 17 pre/stage 0 proteins and 53 stage 1 proteins were trained and tested using Support Vector Machine to assess all potential 3-plexes in order to identify those with mean diagnostic accuracy > 99%. This yielded 5 3-plexes in precancer/stage 0 and 34 3-plexes in stage 1 with near perfect diagnostic accuracy. These plexes are currently being assessed in single analyte and multiplex immunoassays with the goal of translating candidate 3-plexes to the clinical. Conclusions: Key biomarkers from CRC patient EVs indicate the potential to detect and diagnose early-stage and low tumor burden cancers using our methods. Interestingly, the most accurate 3-plexes consist of proteins from immune, inflammatory and metabolic processes, suggesting that for the detection of early stage cancer it may be critical to include both tumor and host specific biomarkers. Citation Format: Poorva Mudgal, Cheryl Bandoski, Zachary Opheim, Martin Mehnert, Valesca Anschau, Issa Isaac, Alan Ezrin, Todd Hembrough. Highly accurate detection of early-stage colorectal cancer using tumor and immune extracellular vesicles biomarkers [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr PR013.

A review on deep learning methods for heart sound signal analysis

IntroductionApplication of Deep Learning (DL) methods is being increasingly appreciated by researchers from the biomedical engineering domain in which heart sound analysis is an important topic of study. Diversity in methodology, results, and complexity causes uncertainties in obtaining a realistic picture of the methodological performance from the reported methods.MethodsThis survey paper provides the results of a broad retrospective study on the recent advances in heart sound analysis using DL methods. Representation of the results is performed according to both methodological and applicative taxonomies. The study method covers a wide span of related keywords using well-known search engines. Implementation of the observed methods along with the related results is pervasively represented and compared.Results and discussionIt is observed that convolutional neural networks and recurrent neural networks are the most commonly used ones for discriminating abnormal heart sounds and localization of heart sounds with 67.97% and 33.33% of the related papers, respectively. The convolutional neural network and the autoencoder network show a perfect accuracy of 100% in the case studies on the classification of abnormal from normal heart sounds. Nevertheless, this superiority against other methods with lower accuracy is not conclusive due to the inconsistency in evaluation.

Improving Rainfall Prediction Accuracy in the USA Using Advanced Machine Learning Techniques

The key aim of this research project is to design and evaluate advanced machine learning models for increasing accuracy in rainfall forecasting over the USA. We intended to investigate nonlinear relationships typical of the atmospheric variables using state-of-the-art ML methods for more accurate rainfall predictions. For this research project on rainfall forecasting in the USA, we utilized an extensive dataset that comprises historical rainfall data collected from the National Oceanic and Atmospheric Administration (NOAA) and other meteorological agencies. The main dataset we use in this paper consists of daily rainfall measurements across various geographical locations of the USA, thus capturing the wide-ranging historical data necessary for both training and validation of the model. Besides measuring rainfall, we included meteorological data from sources such as NOAA's Global Historical Climatology Network and NASA's Modern-Era Retrospective Analysis for Research and Applications. These datasets further provided key variables that are known to affect rain, including temperature, humidity, wind speed, and atmospheric pressure. The performance metrics used in this research work for the models considered include accuracy, precision, recall, and F1 score. The above table shows that the Random Forest Classifier outperformed the other models, achieving perfect accuracy. That indicated that it rightly classified all the instances in the test set. The Logistic Regression and Support Vector Machine models gave a quite good performance by giving above average accuracy but had lower precision and recall for the rainfall prediction. Accurate rainfall forecasting has direct consequences on agriculture, greatly empowering farmers and agricultural planners to make more effective decisions regarding planting, harvesting, and crop management. The forecasts of rainfall are also of critical importance in disaster management regarding planning for flood emergencies. Moreover, precise forecasting of rainfall, particularly in sustainable water resources management, presents the most important data in planning for and conserving these resources.

Microwell-assembled aluminum substrates for enhanced single-cell analysis: A novel approach for cancer cell profiling by Raman spectroscopy

Single-cell analysis is critical for advancing personalized medicine, as it reveals cell population heterogeneity that influences disease outcomes. We present a microwell-assembled aluminum substrate platform that enhances single-cell Raman spectroscopy in liquid suspension by isolating individual cells and preventing stacking and movement, which significantly improves signal stability and the signal-to-noise ratio (SNR).We applied this novel platform to analyze PC-9 lung cancer cells and BEAS-2B normal bronchial epithelial cells, identifying distinct biochemical differences. Notably, cancer cells showed higher levels of adenine, cytochromes, DNA/RNA, and unsaturated lipids, along with an increased unsaturation ratio and protein content. These findings were further validated using machine learning models. An eXtreme Gradient Boosting (XGBoost) model achieved perfect classification accuracy of 100 %, underscoring the robustness of the spectral features identified by our platform.Our platform not only enhances single-cell Raman signal detection but also holds promise for biomedical applications, including early cancer detection, treatment monitoring, and drug development. The high-throughput capacity of this platform featuring over 120,000 wells, along with its compatibility with techniques such as Raman-activated cell sorting (RACS) further extends its potential for clinical diagnostics and personalized medicine.

Diagnostic Accuracy of B-scan Ultrasonography in Detecting Vitreoretinal Pathology after Open-Globe Injury

To assess the diagnostic accuracy of B-scan ultrasonography (echography) after open-globe injury (OGI) repair in detecting vitreoretinal pathology, as confirmed by intraoperative inspection during subsequent pars plana vitrectomy (PPV). Retrospective, single-center, consecutive case series. Patients with OGI treated at the University of Iowa Hospitals and Clinics (UIHC) from February 2018 through December 2023 who underwent OGI repair and had at least one B-scan performed post-repair but prior to subsequent PPV. B-scans were performed by an experienced echographer and reviewed by the managing vitreoretinal surgeon for the presence of vitreous hemorrhage (VH), retinal tear (RT), retinal detachment (RD), choroidal detachment, and vitreoretinal incarceration. B-scan findings were compared to findings on direct inspection during PPV, which served as the gold standard. Sensitivity, specificity, and positive/negative predictive value of B-scan findings. The study included 62 eyes of 61 patients, predominantly with severe OGIs (mean presenting logMAR VA of 2.52 ± 0.41; 75% with Ocular Trauma Score of 1 or 2). B-scan had excellent diagnostic accuracy for VH, but for every other type of vitreoretinal pathology there were significant false positives, false negatives, or both. B-scan sensitivity was particularly low for vitreoretinal incarceration (11%), RT (32%), and RD (78%). This study identified much lower diagnostic accuracy of B-scan ultrasonography after OGI for all vitreoretinal pathologies except VH compared to previous, smaller studies that reported perfect accuracy (100% sensitivity and specificity). Ultrasonography provides useful clinical information but should not be solely relied upon to diagnose or rule out severe vitreoretinal pathology that may prompt vitreoretinal referral and/or PPV after OGI.

Constitutive Modeling of Anisotropic Plasticity with ML Algorithms in Metals

Nuclear reactors hold significant promise for mitigating the environmental crisis and achieving a carbon-free world. The advancement of nuclear technology, however, is intricately tied to the materials used in reactors, with metals and alloys forming their core components. Understanding the plasticity and degradation of these materials is crucial to prevent critical reactor failures caused by factors such as radiation-induced embrittlement, creep, and fuel-cladding interactions. While traditional plasticity modeling has relied on constitutive laws and yield functions, the advent of machine learning (ML) and artificial intelligence has opened new avenues for this field. This paper explores the potential of data-driven approaches to enhance plasticity modeling for metals used in nuclear reactors. Recent studies have leveraged ML algorithms, including support vector machines and artificial neural networks, to model yield surfaces and correct theoretical yield functions with experimental data. Despite their accuracy, these models often lack interpretability and generality. To address these challenges, we investigate the applicability of various ML algorithms, i.e. neural networks, logistic regressions, decision trees, K-nearest neighbors, and Gaussian processes (GPs), in developing data-oriented flow rules for plasticity modeling. The paper demonstrates the superiority of GPs in terms of interpretability and generality. Using stress data generated from PyLabFEA, we compared the performance of these algorithms, highlighting the intrinsic uncertainties associated with GP predictions. Our findings indicate that Gaussian process classifiers (GPCs) offer a promising approach for modeling plasticity in metals, providing a balance between precision and physical insight. Additionally, this work presents a deep neural network (DNN) to model anisotropic Hill-type plasticity with perfect test data accuracy (accuracy score: 1.0), which is often hard to achieve with a classification problem. This shows the superiority of DNNs in terms of accuracy in modeling yield functions. The implications of our results for bridging the scale gaps in macrolevel simulations are discussed, and future directions for incorporating microstructural features into ML-based plasticity models are proposed. Overall, the ML framework presented in this paper can be employed for modeling constitutive relations that can be incorporated within traditional Finite Element Method (FEM)–based codes. Specifically, the GPC or DNN developed in this work can be readily swapped with the yield function within the so-called return mapping algorithm of nonlinear FEM-based plasticity subroutines to indicate yielding. Consequently, this merging of ML and FEM will allow us to leverage the geometric representational ability of FEM alongside the superior modeling capabilities of the ML algorithms.

High Capacity and Reversible Fragile Watermarking Method for Medical Image Authentication and Patient Data Hiding.

The exchange of medical images and patient data over the internet has attracted considerable attention in the past decade, driven by advancements in communication and health services. However, transferring confidential data through insecure channels, such as the internet, exposes it to potential manipulations and attacks. To ensure the authenticity of medical images while concealing patient data within them, this paper introduces a high-capacity and reversible fragile watermarking model in which an authentication watermark is initially generated from the cover image and merged with the patient's information, photo, and medical report to form the global watermark. This watermark is subsequently encrypted using the chaotic Chen system technique, enhancing the model's security and ensuring patient data confidentiality. The cover image then undergoes a Discrete Fourier Transform (DFT) and the encrypted watermark is inserted into the frequency coefficients using a new embedding technique. The experimental results demonstrate that the proposed method achieves great watermarked image quality, with a PSNR exceeding 113dB and an SSIM close to 1, while maintaining a high embedding capacity of 3 BPP (Bits Per Pixel) and offering perfect reversibility. Furthermore, the proposed model demonstrates high sensitivity to attacks, successfully detecting tampering in all 18 tested attacks, and achieves nearly perfect watermark extraction accuracy, with a Bit Error Rate (BER) of 0.0004%. This high watermark extraction accuracy is crucial in our situation where patient data need to be retrieved from the watermarked images with almost no alteration.

A Portable Electronic Nose Coupled with Deep Learning for Enhanced Detection and Differentiation of Local Thai Craft Spirits

In this study, our primary focus is the biomimetic design and rigorous evaluation of an economically viable and portable ‘e-nose’ system, tailored for the precise detection of a broad range of volatile organic compounds (VOCs) in local Thai craft spirits. This e-nose system is innovatively equipped with cost-efficient metal oxide gas sensors and a temperature/humidity sensor, ensuring comprehensive and accurate sensing. A custom-designed real-time data acquisition system is integrated, featuring gas flow control, humidity filters, dual sensing/reference chambers, an analog-to-digital converter, and seamless data integration with a laptop. Deep learning, utilizing a multilayer perceptron (MLP), is employed to achieve highly effective classification of local Thai craft spirits, demonstrated by a perfect classification accuracy of 100% in experimental studies. This work underscores the significant potential of biomimetic principles in advancing cost-effective, portable, and analytically precise e-nose systems, offering valuable insights into future applications of advanced gas sensor technology in food, biomedical, and environmental monitoring and safety.

Measuring the solubility of tacrolimus in supercritical carbon dioxide (binary and ternary systems), comparing the performance of machine learning models with conventional models

The solubility of tacrolimus in a supercritical carbon dioxide (SC-CO2) with and without co-solvent (ternary and binary system) was investigated. The experimental conditions include the temperatures of 308 to 338 K and the pressures of 12 to 30 MPa were considered. The values of tacrolimus solubilization in SC-CO2 in binary and ternary systems were from 0.416 × 10−5 to 1.589 × 10−5 and from 0.389 × 10−4 to 0.875 × 10−4, respectively. The obtained results presented that adding a co-solvent (ethanol) could affect tacrolimus solubility significantly, the mole fraction of tacrolimus in SC-CO2/co-solvent under all experimental conditions was at least 4.9 more than that of SC-CO2 alone. The correlation of the solubility data was achieved by three distinct methods: (1) SRK EoS with van der Waals (vdW2) and Wong-Sandler (WS) as mixing rules, (2) solution theory, (3) empirical and (4) machine learning based models. The analysis of variance (ANOVA) results showed that Sparks et al. (AIC −817.05, and AARD 5.08 %) and Bian et al. (AIC −815.77 and AARD 5.17 %) models could correlate the experimental data of tacrolimus in SC-CO2 with more accuracy, as compared to the other empirical models. Sodeifian and Sajadian model with AARD% and AIC of 2.99 and −726.22, respectively, has acted as the most accurate empirical model in ternary system. The solution theory was able to calculate the solubility for the tacrolimus-SC-CO2-ethanol system with high accuracy (AARD, % 4.85, AIC −701.41, F value 169.70 and Radj 0.9622). Also, the Gradient Boosting model (AARD accurately predicted the outcomes with perfect accuracy for binary and ternary systems. Finally, total (ΔHtotal, 23.21 cal/mol), solvation (ΔHsol, −19.47 cal/mol) and vaporization (ΔHvap, 42.68 cal/mol) enthalpies were obtained by using an energy term in Chrastil and Bartle models.

Analytical parametric analysis of profiled composite walls reinforced with embedded tubes subjected to axial loading

This paper presents analytical analysis of the axial load behavior of a profiled composite wall (PCW) consisting of two profiled steel sheets (PSSs) filled with concrete and reinforced with/without embedded cold-formed steel tubes (CFSTs). This type of composite wall was designed to resist in-plane axial loading (bearing wall). Three experimental samples were tested and its results were presented in this research. The ultimate axial loads of 42 PCW FE models were calculated based on a published work done by the authors earlier. Comparisons between the results of the FE models via the proposed methods and those of Eurocode 4, BS5400, AISC, Hossain (2000), and Wright (1998) were made. Applied calculation formulas for the ultimate axial load were presented via regression analysis theory on the basis of the verified FE results. Comparisons were made between the developed formulas and existing experimental tests, such as those of Hossain (2000) and Wright (1998) and an experimental study performed by the author, and perfect accuracy was achieved. The results of this study proved that the effects of the embedded CFSTs reduced local buckling of the PSS and increased ductility with increasing axial load resistance of the PCW. The provided formulas accurately predict the axial load of the PCWs.

Predicting Bacterial Antibiotic Resistance using MALDI-TOF Mass Spectrometry Databases with ELM Applications.

Early detection of antibiotic resistance is a crucial task, especially for vulnerable patients under prolonged treatments with a single antibiotic. To solve this, machine learning approaches have been reported in the state of art. Researchers have used MALDI-TOF MS in order to predict antibiotic resistance and/or susceptibility in bacterial samples. Weis, et al. implemented LR, LightGBM and ANN to study the antibiotic resistance on bacterial strains of Escherichia Coli, Staphylococcus Aureus, and Klebsiella Pneumoniae. Despite promising results, the models have not achieved perfect accuracy, specifically when the classes are unbalanced. On the other hand, Extreme Learning Machine (ELM) is a training algorithm for forward propagation of single hidden layer neural networks, which converges much faster than traditional methods and offers promising performance along with less programmer intervention. In this way, this study introduced improved ELMs, including two weighted ELMs proposed by Zong, and the SMOTE technique in order to create new synthetic samples of the minority class. After heuristic optimization of ELM hiper-parameters, results demonstrated 85% in accuracy and 85% in geometric mean for the classification problem in the case of weighted ELM 1 subject to the SMOTE technique of oversampling.

Towards invertible 2D crystal structure representation for efficient downstream task execution

Abstract In the study of MoS2 lattice defects, we explore the use of Siamese Neural Networks (SNN) to create invariant embeddings, which respect the crystalline symmetry of the lattice. By training our model with contrastive learning, we successfully differentiate configurations with varying defects, achieving perfect accuracy in recognizing equivalent placements. Our method showcases the capability to predict physical properties like formation energy per site and the bandgap with strong performance across both low and high-defect density scenarios, outperforming traditional methods when enhanced with polynomial features. Despite its effectiveness, the model presents limitations at high defect densities, indicating a need for further refinement. Our approach lays the groundwork for reverse-engineering processes. Thus, we open pathways for generative models that can navigate from specified property ranges to optimal defect configurations, fostering an efficient solution-space exploration for bespoke material synthesis.

Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns

Customer retention is a key priority for mobile telecommunications companies, as acquiring new customers is significantly more costly than retaining existing ones. A major challenge in this field is predicting customer churn—users discontinuing services. Traditional predictive models such as rule-based systems often struggle with the complex, non-linear nature of customer behavior. To address this, we propose the use of deep learning techniques, specifically multi-layer perceptron (MLP) and radial basis function (RBF) networks, to improve the accuracy of churn predictions. However, while neural networks excel in predictive performance, they are often criticized for being “black-box” models, lacking interpretability. A real-world data set is considered, which originally contained information about 15,000 randomly selected clients. Various network structures and configurations are analyzed. The obtained results are compared with results generated using fuzzy rule-based and rough-set rule-based systems. The MLP model achieved an almost perfect accuracy of 0.999 with an F-measure of 0.989, outperforming traditional methods such as fuzzy rule-based and rough-set systems. Although the RBF model slightly lagged in accuracy, it demonstrated a superior recall of 0.993, indicating better identification of potential churners. These results demonstrate that neural network models significantly enhance predictive performance in churn modeling. The interpretability of the model is also discussed since it bears significance in real applications. Our contribution lies in showing that deep learning methods significantly enhance churn prediction accuracy, though the challenge of model interpretability remains a critical area for future work.

Comparing Machine Learning Techniques for Detecting Chronic Kidney Disease in Early Stage

CKD is a gradual disease that affects millions of people throughout the United States and results in high morbidity and mortality rates. Chronic Kidney Disease is an ailment that culminates in a gradual loss of kidney function over t,ime. Early detection is essential since timely interventions may prevent the progression of CKD, improve outcomes and survival for patients with CKD, and reduce healthcare costs. In the recent decade, machine learning models have emerged as a game-changing tool in medical diagnostics, leveraging big data and complex algorithms to find patterns almost invisible to clinicians and physicians. This study deployed and evaluated various machine learning approaches for the early detection of CKD, focusing on their comparative performance, strengths, and weaknesses. Machine learning transforms medical diagnosis by leveraging big data and sophisticated algorithms to find patterns that might otherwise elude healthcare professionals. The dataset used for this research will be the CKD dataset, which was contributed to by the Cleveland Clinic in 2021. The dataset can be accessed publicly through the University of California, Irvine's UCI Machine Learning Repository. In this project, the analyst compared and contrasted the performance of Logistic Regression, Decision Trees, and Random Forests. Experimentation results demonstrated that logistic regression had the best performance, yielding a perfect F1 score and accuracy, closely followed by random forest. This result showed that the Logistic model ideally classified all the instances in the test set. Consolidating machine learning algorithms into the early detection of Chronic Kidney Disease (CKD) holds substantial promise for transforming clinical practice. Healthcare professionals can enhance diagnostic accuracy and facilitate timely interventions by leveraging proposed algorithms such as logistic regression.

Postoperative Otoplasty Care With ChatGPT-4: A Study on Artificial Intelligence (AI)-Assisted Patient Concern and Education.

Otoplasty is a cosmetic surgery that is performed to alter the size, shape, or position of the ear by using permanent stitches. Its main purpose is to correct protruding ears, a condition known as prominauris. After the surgery, it is crucial to provide proper care to ensure successful recovery. However, obtaining timely medical advice can be difficult, especially in remote areas or places with limited resources. To address this issue, incorporating advanced artificial intelligence (AI) tools like Chat Generative Pre-trained Transformer (ChatGPT)-4 into postsurgical care could help fill the gap in patient education and support. This study aims to assess whether ChatGPT4 can be a reliable, accurate, and effective method for answering the most common patient questions and concerns post-otoplasty. The main objective was to assess the AI chatbot's capacity to deliver precise, concise, and pertinent information, especially in situations where health care professionals are limited in availability. In this study, over 50 patients were engaged, and ChatGPT4 was employed to present the same 5 common postoperative questions post-otoplasty surgery care. The AI chatbot's responses were analyzed for accuracy, response time, clarity, and relevance. The chatbot could potentially provide timely assistance, answer questions, and address concerns related to postsurgical care in otoplasty. The responses exhibited a perfect accuracy rate of 100%, closely corresponding to existing medical guidelines. This study explores the potential of AI-driven solutions to enhance patient education and support, especially in areas where access to health care professionals may be limited. However, professional medical advice is crucial in postoperative care and cannot be replaced by ChatGPT-4. By leveraging AI tools like chatbots, individuals in remote or resource-limited settings can potentially receive valuable information and guidance, contributing to successful rehabilitation and overall health care outcomes. Ethical considerations around the use of AI in health care must also be carefully addressed to ensure patient privacy, data security, and appropriate clinical oversight.

Improving rule-based classifiers by Bayes point aggregation

The widespread adoption of artificial intelligence systems with continuously higher capabilities is causing ethical concerns. The lack of transparency, particularly for state-of-the-art models such as deep neural networks, hinders the applicability of such black-box methods in many domains, like the medical or the financial ones, where model transparency is a mandatory requirement, and hence white-box models are largely preferred over potentially more accurate but opaque techniques.For this reason, in this paper, we focus on ruleset learning, arguably the most interpretable class of learning techniques. Specifically, we propose Bayes Point Rule Classifier, an ensemble methodology inspired by the Bayes Point Machine, to improve the performance and robustness of rule-based classifiers. In addition, to improve interpretability, we propose a technique to retain the most relevant rules based on their importance, thus increasing the transparency of the ensemble, making it easier to understand its decision-making process.We also propose FIND-RS, a greedy ruleset learning algorithm that, under mild conditions, guarantees to learn hypothesis with perfect accuracy on the training set while preserving a good generalization capability to unseen data points.We performed extensive experimentation showing that FIND-RS achieves state-of-the-art classification performance at the cost of a slight increase in the ruleset complexity w.r.t. the competitors. However, when paired with the Bayes Point Rule Classifier, FIND-RS outperforms all the considered baselines.

Development of a novel light-sensitive PPG model using PPG scalograms and PPG-NET learning for non-invasive hypertension monitoring

Background and objectivePhotoplethysmography (PPG) signals provide a non-invasive method for monitoring cardiovascular health, including blood pressure levels, which are critical for the early detection and management of hypertension. This study leverages wavelet transformation and special purpose deep learning model, enhanced by signal processing and normalization, to classify blood pressure stages from PPG signals. The primary objective is to advance non-invasive hypertension monitoring, improving the accuracy and efficiency of these assessments. MethodsThe study employed continuous wavelet transform (CWT) to prepare PPG signals for analysis using a special purpose PPG-NET designed by applying advanced deep-learning models. PPG-NET was verified by applying several pre-trained models, including Inception, MobileNetV2, InceptionResNetV2, and others to the PPG data. Rigorously five-fold cross-validated models were conducted to obtain the models' performance to ensure robustness and repeatability of results. ResultsThe PPG-NET model demonstrated superior performance, achieving a perfect accuracy of 100 % in classifying the four stages of hypertension—normal, prehypertension, stage 1, and stage 2. The evaluation metrics reported include precision, sensitivity, and specificity, with the PPG-NET model achieving 100 % across all metrics. Other models showed varying levels of accuracy, with InceptionV3 also reaching 91.5 %, while some, like VGG-19, underperformed significantly. ConclusionsIntegrating CWT and PPG-NET offers a promising avenue for enhancing non-invasive blood pressure monitoring. The PPG-NET model, in particular, showed potential for clinical application due to its high accuracy and reliability. This study showed the effectiveness of combining advanced computational techniques with traditional PPG analysis, potentially leading to more personalized and accessible hypertension management strategies.

Early-stage cardiomegaly detection and classification from X-ray images using convolutional neural networks and transfer learning

Cardiomyopathy is a serious condition that can result in heart failure, sudden cardiac death, malignant arrhythmias, and thromboembolism. It is a significant contributor to morbidity and mortality globally. The initial finding of cardiomegaly on radiological imaging may signal a deterioration of a known heart condition, an unknown heart disease, or a heart complication related to another illness. Further cardiological evaluation is needed to confirm the diagnosis and determine appropriate treatment. A chest radiograph (X-ray) is the main imaging method used to identify cardiomegaly when the heart is enlarged. A prompt and accurate diagnosis is essential to help healthcare providers determine the most appropriate treatment options before the condition worsens. This study aims to utilize convolutional neural networks and transfer learning techniques, specifically Inception, DenseNet-169, and ResNet-50, to classify cardiomegaly from chest X-ray images automatically. The utilization of block-matching and 3D filtering (BM3D) techniques aimed at enhancing image edge retention, decreasing noise, and utilizing contrast limited adaptive histogram equalization (CLAHE) to enhance contrast in low-intensity images. Gradient-weighted Class Activation Mapping (GradCAM) was used to visualize the significant activation regions contributing to the model's decision. After evaluating all the models, the ResNet-50 model showed outstanding performance. It achieved perfect accuracy of 100 % in both training, and validation, and an impressive 99.8 % accuracy in testing. Additionally, it displayed complete 100 % precision, recall, and F1-score. These findings demonstrate that ResNet-50 surpassed all other models in the study. As a result, the impressive performance of the ResNet-50 model suggests that it could be a valuable tool in improving the efficiency and accuracy of cardiomyopathy diagnosis, ultimately leading to better patient outcomes.

UNVEILING GENDER FROM INDONESIAN NAMES USING RANDOM FOREST AND LOGISTIC REGRESSION ALGORITHMS

Gender detection can be done in many ways, some of these ways by using image identification such as the process of image identification based on faces or image shapes, on the other hand image identification and detection can also be done based on text or written data. The usefulness of gender identification can be used in various aspects of life, ranging from greetings such as ladies and gentlemen, which will certainly make the person concerned feel more appreciated by the accuracy of the pronunciation of the name. This gender identification and detection process can be done by making class predictions on predetermined gender label classes. Of course, each name in various languages has different characteristics in identifying and representing each gender, as well as Indonesian names that have diversity and unique levels of variation. The purpose of this study is to test the results of the algorithm in classification based on class labels. The application of this detection uses two algorithms, namely Random Forest and Logistic Regression. Both of these algorithms can predict classes with perfect accuracy in 6 experimental data, then the results of 526 experimental data resulted in a final accuracy of 0.94 for logistic regression and 0.93 for random forest. The advantage with a thin difference in this case is in the Logistic Regression algorithm.

A Convolutional Neural Network-based Intelligence System for the Identification of Copra Maturity Levels

The North Sulawesi Province, widely recognized as the Coconut Waving Province owing to its substantial coconut tree population, primarily depends on copra production. This research presents a novel methodology for determining copra maturity levels by utilizing a Convolutional Neural Network (CNN) on digital photographs, classifying them into three distinct stages: raw, half-ripe, and ripe. By employing a rigorous 10-fold cross-validation technique, our models demonstrated remarkable performance. Notably, even the model with the lowest performance achieved a commendable accuracy of 87.78% during the training and validation phases. The model that exhibited the highest level of performance achieved a perfect accuracy rate of 100%. Moreover, when subjected to real-world testing situations using novel data, the model with the lowest performance exhibited a noteworthy accuracy of 83.34%. In contrast, the highest-performing model achieved a flawless accuracy of 100%. Based on the findings above, an online system has been built that leverages the most optimal model, facilitating the assessment of copra maturity in real-time. The prospects encompass the integration of this methodology into copra sorting machinery, thereby yielding advantages for both agricultural producers and industrial sectors. This research enhances copra quality control processes and promotes sustainability in the copra industry. Further research could explore refining the CNN model to accommodate a broader range of copra variations and investigating automation possibilities in copra production processes. These endeavors would advance the efficacy and applicability of copra maturity classification methods, fostering continued innovation in the industry.