Explore Machine Learning Research Articles

The Impact of Artificial Intelligence on Cardiovascular Disease Diagnosis: A Review

Background: Cardiovascular diseases present a significant global health challenge and remain the leading cause of death worldwide. However, traditional approaches to prevention, diagnosis, and treatment struggle to keep up with the increasing prevalence of these diseases. Aim: To enhance patient outcomes and optimize healthcare resource utilization. Artificial intelligence (AI), specifically machine learning and deep learning, has rapidly emerged as a promising tool with the potential to revolutionize various aspects of cardiovascular disease management, including detection, diagnosis, and treatment. Method: Reviewed the current literature surrounding AI techniques using PubMed, Science Direct, NCBI and Google Scholar, specifically exploring machine learning and deep learning, and their application in diagnosing heart disease. The focus was on AI's role in improving diagnostic techniques such as echocardiography, cardiac magnetic resonance imaging, computed tomography angiography, and electrocardiogram analysis. Results: AI has promising applications in various aspects of cardiovascular disease management. Its application in diagnostic techniques can help detect, diagnose, and treat heart disease, ultimately leading to more accurate and personalized treatments. Practical Implication: By integrating these advanced technologies into clinical practice, we can transform the diagnosis and management of heart diseases, leading to more accurate and personalized diagnostics and treatments. Conclusion: AI presents a significant potential in transforming the global health landscape by enhancing cardiovascular disease management. By leveraging these advanced technologies, clinicians can improve patient care and overall outcomes while addressing the increasing prevalence of these diseases. Keywords: Heart Diseases, Diagnosis, Deep Learning, Machine Learning, Public Health.

Bioinformatics Applications in Chronic Diseases: A Comprehensive Review of Genomic, Transcriptomics, Proteomic, Metabolomics, and Machine Learning Approaches

This manuscript provides a detailed exploration of the pivotal role that bioinformatics plays in elucidating the intricate molecular landscape associated with chronic diseases. Emphasizing the significance and prevalence of these enduring health issues, the introduction establishes the broader context of bioinformatics in chronic disease research. This review systematically covers the application of bioinformatics tools and techniques in comprehending, identifying, and managing chronic illnesses. The first section highlights the importance of genetics and genomics, detailing the utilization of genomic data and advancements in genetic biomarker discovery. Subsequently, the discussion extends to transcriptomics and gene expression, encompassing profiling methods, the identification of dysregulated genes, and the regulatory functions of non-coding RNA in long-term illnesses.Moving forward, the manuscript delves into proteomics, elucidating protein-protein interaction networks, associated tools and techniques, and post-translational modifications. This comprehensive coverage aims to provide readers with a nuanced understanding of the molecular complexities underlying chronic diseases. The subsequent section focuses on metabolomics and metabolic pathways, with an emphasis on the clinical utility of metabolite biomarkers, changes in metabolic pathways, and techniques for characterizing diseases. Following this, the manuscript explores machine learning applications in bioinformatics, providing insights into their role in enhancing our understanding of chronic diseases. The later part of the manuscript addresses practical applications and case studies, showcasing disease-specific bioinformatics tools, databases, and the broader utility of research findings. Additionally, the penultimate section examines privacy, ethical considerations, and data quality concerns, addressing challenges and potential paths for the field of bioinformatics. In conclusion, the manuscript discusses forthcoming trends and prospective research directions, contributing to the advancement of bioinformatics research in chronic illnesses. Overall, this review provides a comprehensive overview of the multifaceted applications of bioinformatics in chronic disease research.

Method—An Investigation Into Post-Hoc Analysis Methods for Electrochemical Biosensor Data

Recently, researchers are exploring machine learning (ML) algorithms as post-hoc analysis tools to improve performances of electrochemical biosensors (EBs). While reported results are promising, yet comprehensive study on optimal methods for model development is still lacking. For improved efficiency, accuracy, and robustness, it is essential to optimise the relationships between feature extraction techniques and choice of training algorithms. Herein, this paper presents a comparative study between different feature extractions methods, namely principal component analysis (PCA), linear discriminative analysis (LDA), fast Fourier transform (FFT) and discrete wavelet transform (DWT), to compress and extract significant components from differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) datasets. Support Vector Regression (SVR) and Multilayer Perceptron (MLP) models were developed, and their performances were compared with conventional post-analysis methods. The best performing combination for DPV dataset was MLP with DWT, achieving an R2 of 0.995, and for EIS dataset was MLP with PCA, achieving an R2 of 0.960, on test set, respectively. The developed models had achieved an average of 0.61% improvement for real sample recovery tests. The presented approaches demonstrated the capabilities of optimised ML models to automate post hoc analysis for more robust outcomes, while eliminating tedium of post-analysis for end users.

PERBANDINGAN ALGORITMA MACHINE LEARNING UNTUK ANALISIS SENTIMEN PADA ULASAN HOTEL

The paper extensively explores machine learning algorithms for evaluating sentiments in hotel reviews, particularly within the tourism and hospitality industry. It underscores the importance of precise reviews in utilizing artificial intelligence for improved operational efficiency, revenue optimization, and heightened customer satisfaction. Notably, supervised machine learning algorithms like Gradient Boosting, Support Vector Machine, and K-Nearest Neighbor are highlighted for offering recommendations based on reviews to predict user preferences. The research methodology involves data scraping, cleaning, preprocessing, and labeling, followed by training and testing the chosen machine learning algorithms. Results indicate that the Support Vector Machine algorithm demonstrated superior performance with accuracy 0.8553, precision 0.8433, recall 0.8553, dan F1-score 0.8424, suggesting its appropriateness for sentiment analysis in hotel reviews. The paper concludes by recommending the implementation of the Support Vector Machine model for sentiment analysis in hotel reviews in Palangka Raya, Indonesia, and proposes avenues for further industry development and enhancement.

Exploring Exploring Machine Learning and Deep Learning Approaches for Disaster Prediction and Management: A Survey of Different Approaches

All around the world, natural disasters have an impact on both human and animal life. In addition to causing major harm to property, animal life, etc. Natural disasters (including landslides, cloudbursts, heat waves, hurricanes, tsunamis, floods, tsunamis, earthquakes, and wildfires) affect thousands of lives each year throughout the world. Twitter, a social networking platform, users can share news, opinions, and personal stories. Due to the widespread availability of real-time data, numerous service agencies regularly analyze this data to spot crises, lower risk, and save lives. Humans, however, are unable to manually filter through the vast number of records and spot hazards in real-time to achieve this, it has been suggested in numerous studies to provide words in forms that computers can understand and on the word representations, use machine learning techniques to determine the meaning behind a post with accuracy. The community can monitor disasters by reporting hazards that are related to disaster occurrences on social media, which has been essential to emergency preparedness. This study examines how social media platform Twitter might be used in disaster-related research. It focuses on the most recent machine learning, deep learning, and disaster prediction techniques. Acquiring a thorough grasp of the numerous data kinds and their sources in relation to a variety of tasks and crisis management scenarios is another goal of the work. Additionally, The study also aims to offer a comprehensive analysis of the various data mining methods utilized for tackling various issues related to natural disasters as well as comprehensive directions on how to categorize tweets as "Related to Catastrophe" or "Not related with Catastrophe" using natural processing methods.

Exploring machine learning and statistical approach techniques for landslide susceptibility mapping in Siwalik Himalayan Region using geospatial technology.

Landslides are a natural threat that poses a severe risk to human life and the environment. In the Kumaon mountains region in Uttarakhand (India), Nainital is among the most vulnerable areas prone to landslides inflicting harm to livelihood and civilization due to frequent landslides. Developing a landslide susceptibility map (LSM) in this Nainital area will help alleviate the probability of landslide occurrence. GIS and statistical-based approaches like the certainty factor (CF), information value (IV), frequency ratio (FR) and logistic regression (LR) are used for the assessment of LSM. The landslide inventories were prepared using topography, satellite imagery, lithology, slope, aspect, curvature, soil, land use and land cover, geomorphology, drainage density and lineament density to construct the geodatabase of the elements affecting landslides. Furthermore, the receiver operating characteristic (ROC) curve was used to check the accuracy of the predicting model. The results for the area under the curves (AUCs) were 87.8% for logistic regression, 87.6% for certainty factor, 87.4% for information value and 84.8% for frequency ratio, which indicates satisfactory accuracy in landslide susceptibility mapping. The present study perfectly combines GIS and statistical approaches for mapping landslide susceptibility zonation. Regional land use planners and natural disaster management will benefit from the proposed framework for landslide susceptibility maps.

Enhancing Renewable Energy Storage Conversion Efficiency using ERFE with FFNN

The 21st century witnesses a pivotal global shift towards Renewable Energy Sources (RES) to combat climate change. Nations are adopting wind, solar, hydro, and other sustainable energy forms. However, a primary concern is the inconsistent nature of these sources. Daily fluctuations, seasonal changes, and weather conditions sometimes make renewables like the sun and wind unreliable. The key to managing this unpredictability is efficient Energy Storage Systems (ESS), ensuring energy is saved during peak periods and used during low production times. However, existing ESSs are not flawless. Energy conversion and storage inefficiencies emerge due to temperature changes, inconsistent charge rates, and voltage fluctuations. These challenges diminish the quality of stored energy, resulting in potential waste. There is a unique chance to address these inefficiencies using the vast data from renewable systems. This research explores Machine Learning (ML), particularly Neural Networks (NN), to improve REES efficiencies. Analyzing data from Palm Springs wind farms, the study employs an Entropy-Based Recursive Feature Elimination (ERFE) coupled with Feed-Forward Neural Networks (FFNN). ERFE utilizes entropy to prioritize essential features, reducing redundant data and computational demands. The tailored FFNN then predicts energy conversion rates, aiming to enhance energy storage conversion and maximize the usability of generated Renewable Energy (RE).

Automated Narrative Craft: Exploring Machine Learning in Story Generation.

Automated Narrative Craft: Exploring Machine Learning in Story Generation.

Exploring Machine Learning and Spider Communication Research: Deciphering the Vibrations of Spiders

Exploring Machine Learning and Spider Communication Research: Deciphering the Vibrations of Spiders

An experiment-based investigation into machine learning for predicting coronary heart disease

Extensive inquiry has been conducted to explore potential applications of machine learning methodologies in the realm of cardiovascular disease management. To facilitate a more comprehensive investigation This study explores machine learning algorithms, specifically Support Vector Machines (SVM) and Artificial Neural Networks (ANN), for disease identification, focusing on cardiovascular diseases. Utilizing a Kaggle dataset of around seventy thousand medical records, the research aims to refine methodology and assess performance variations. SVM and ANN techniques are applied to the Kaggle dataset, revealing SVM accuracies of 0.9997 (default), 0.9998 (RBF kernel, C=100.0), and 1.000 (linear kernel, C=1000.0). The Feedforward neural network, using Adam optimization across 50 batches and 10 epochs, achieved perfect accuracy of 1.000.

Exploring Machine Learning Algorithms for Malicious Node Detection Using Cluster-Based Trust Entropy

Exploring Machine Learning Algorithms for Malicious Node Detection Using Cluster-Based Trust Entropy

Advancing Computational Fluid Dynamics through Machine Learning: A Review of Data-Driven Innovations and Applications

This review explores Machine Learning (ML) integration with Computational Fluid Dynamics (CFD) to enhance simulation accuracy and efficiency. CFD has long been the standard for fluid flow analysis, but its computational cost and limitations in handling complex flows have spurred interest in data-driven approaches. With its ability to model nonlinear relationships, machine learning offers a promising solution. The review covers vital methodologies, including data acquisition from CFD simulations, feature extraction, and the selection of ML models such as neural networks and supervised learning algorithms. Furthermore, it examines the application of ML in accelerating CFD simulations and improving turbulence modeling. While promising, challenges remain, particularly in ensuring ML models respect the underlying physical laws governing fluid dynamics. The review concludes by discussing future research directions, particularly in physics-informed machine learning, which seeks to integrate physical constraints into the ML process. This combination of data-driven and physics-based approaches can transform CFD applications across industries.

Exploring machine learning techniques for oil price forecasting: A comparative study of SVM, SMO, and SGD-base models

Crude oil prices play a critical role in the global economy, and accurately predicting their future values is of great importance. In this study, we evaluated the performance of three support vector machine (SVM) models - standard SVM, SMO-based SVM, and SGD-based SVM - in predicting crude oil prices based on daily, weekly, and monthly data, aligning with the Industry 4.0 paradigm, to enhance decision-making in the oil and gas sector. The results showed that all three models performed well, with high coefficient of determination R2 values and low MSE values across all versions of the dataset. The SMO and SGD-based models demonstrated particular advantages for larger datasets. Furthermore, SVMs are well-suited for modelling complex financial datasets and can handle non-linear relationships between variables. Our findings suggest that SVMs can provide effective models for predicting crude oil prices, with the SMO-based and SGD-based models showing potential for large datasets.

МЕТОДИ ТА МОДЕЛІ DATA SCIENCE В СУЧАСНІЙ ЕКОНОМІЦІ

In contemporary economics, data science models play a crucial role in analyzing complex relationships, predicting economic trends, and informing policy decisions. This article reviews the most commonly used data science models in economics, including econometric models like linear and logistic regression, Probit and Tobit models, time series analysis models such as ARIMA and Vector Autoregression (VAR), and panel data analysis methods like fixed and random effects models and Difference-in-Differences (DiD). Additionally, it explores machine learning algorithms, clustering and classification techniques, dimensionality reduction methods, Bayesian methods, and natural language processing (NLP) applications. The article highlights their purposes, applications, and relevant works, emphasizing the strengths and limitations of each model. It also discusses the impact of these models across various sectors, including finance, retail, energy, and healthcare. This comprehensive overview underscores the importance of aligning data science models with business objectives, ensuring data quality, investing in scalable technologies, fostering a data-driven culture, and addressing ethical considerations. The article concludes with future research directions, such as advanced neural network architectures, large language models, generative AI models, hybrid models, and the need for interpretable and ethical AI applications in economics. The importance of this topic lies in the transformative potential of data science models to enhance economic analysis and decision-making. By leveraging advanced data science techniques, economists can gain deeper insights into complex economic phenomena, improve forecasting accuracy, and develop more effective policies. As data-driven approaches continue to evolve, they provide powerful tools for addressing critical economic challenges, driving innovation, and fostering sustainable growth across various sectors.

Exploring Machine Learning for Semiconductor Process Optimization: A Systematic Review

Exploring Machine Learning for Semiconductor Process Optimization: A Systematic Review

Utilizing machine learning for early screening of thyroid nodules: a dual-center cross-sectional study in China.

Thyroid nodules, increasingly prevalent globally, pose a risk of malignant transformation. Early screening is crucial for management, yet current models focus mainly on ultrasound features. This study explores machine learning for screening using demographic and biochemical indicators. Analyzing data from 6,102 individuals and 61 variables, we identified 17 key variables to construct models using six machine learning classifiers: Logistic Regression, SVM, Multilayer Perceptron, Random Forest, XGBoost, and LightGBM. Performance was evaluated by accuracy, precision, recall, F1 score, specificity, kappa statistic, and AUC, with internal and external validations assessing generalizability. Shapley values determined feature importance, and Decision Curve Analysis evaluated clinical benefits. Random Forest showed the highest internal validation accuracy (78.3%) and AUC (89.1%). LightGBM demonstrated robust external validation performance. Key factors included age, gender, and urinary iodine levels, with significant clinical benefits at various thresholds. Clinical benefits were observed across various risk thresholds, particularly in ensemble models. Machine learning, particularly ensemble methods, accurately predicts thyroid nodule presence using demographic and biochemical data. This cost-effective strategy offers valuable insights for thyroid health management, aiding in early detection and potentially improving clinical outcomes. These findings enhance our understanding of the key predictors of thyroid nodules and underscore the potential of machine learning in public health applications for early disease screening and prevention.

경기종합지수 단기 예측모형 구축 연구

After the declaration of the ‘Endemic,’ it is crucial to understand the cyclical fluctuations and turning points in the overall economic status of the nation. To promptly and accurately grasp the magnitude and speed of these economic changes, a short-term forecasting analysis was conducted using the composite index of business indicators data from January 2003 to April 2023. In this study, a two-step analysis was performed to propose a standardized forecasting process.
 Firstly, for short-term forecasting of the composite index of business indicators, the results of time series analyses considering only endogenous variables were compared with those incorporating exogenous variables. Subsequently, the study explored machine learning methods, which have been actively discussed recently, and compared and analyzed their prediction performance with traditional time series analysis methods. The analysis revealed that the addition of exogenous variables in time series analysis resulted in higher prediction accuracy compared to analyses considering only endogenous variables. Furthermore, machine learning models exhibited superior prediction performance compared to traditional time series models. As a result of the analysis with the Time-GAN model, which has the best predictive performance, it was found that there would be a rise for 1-2 months due to the growth of private consumption caused by the endemic, but it would decrease again in the 3rd month.

Identifying professional photographers through image quality and aesthetics in Flickr

AbstractIn our generation, there is an undoubted rise in the use of social media and specifically photo and video sharing platforms. These sites have proved their ability to yield rich data sets through the users’ interaction which can be used to perform a data‐driven evaluation of capabilities. Nevertheless, this study reveals the lack of suitable data sets in photo and video sharing platforms and evaluation processes across them. In this way, our first contribution is the creation of one of the largest labelled data sets in Flickr with the multimodal data which has been open sourced as part of this contribution. It incorporates multimodal data, combining information from various sources such as user profiles, photo metadata, and crowdsourced features. Predicated on these data, we explored machine learning models and concluded that it is feasible to properly predict whether a user is a professional photographer or not based on self‐reported occupation labels and several feature representations out of the user, photo and crowdsourced sets. We also examined the relationship between the aesthetics and technical quality of a picture and the social activity of that picture. Finally, we depicted which characteristics differentiate professional photographers from non‐professionals. As far as we know, the results presented in this work represent an important novelty for identifying expertise in the domain of photography, which researchers from various domains can utilise for related applications.

Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset

Abstract Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.

Artificial intelligence applied to analyzes during the pandemic: COVID-19 beds occupancy in the state of Rio Grande do Norte, Brazil.

The COVID-19 pandemic is already considered one of the biggest global health crises. In Rio Grande do Norte, a Brazilian state, the RegulaRN platform was the health information system used to regulate beds for patients with COVID-19. This article explored machine learning and deep learning techniques with RegulaRN data in order to identify the best models and parameters to predict the outcome of a hospitalized patient. A total of 25,366 bed regulations for COVID-19 patients were analyzed. The data analyzed comes from the RegulaRN Platform database from April 2020 to August 2022. From these data, the nine most pertinent characteristics were selected from the twenty available, and blank or inconclusive data were excluded. This was followed by the following steps: data pre-processing, database balancing, training, and test. The results showed better performance in terms of accuracy (84.01%), precision (79.57%), and F1-score (81.00%) for the Multilayer Perceptron model with Stochastic Gradient Descent optimizer. The best results for recall (84.67%), specificity (84.67%), and ROC-AUC (91.6%) were achieved by Root Mean Squared Propagation. This study compared different computational methods of machine and deep learning whose objective was to classify bed regulation data for patients with COVID-19 from the RegulaRN Platform. The results have made it possible to identify the best model to help health professionals during the process of regulating beds for patients with COVID-19. The scientific findings of this article demonstrate that the computational methods used applied through a digital health solution, can assist in the decision-making of medical regulators and government institutions in situations of public health crisis.