Application Of Machine Learning Techniques Research Articles

Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology.

Metal-organic frameworks (MOFs) have settled in the scientific community over the last decades as versatile materials with several applications. Among those, zeolitic imidazolate framework 8 (ZIF-8) is a well-known MOF that has been applied in various and diverse fields, from drug-delivery platforms to microelectronics. However, the complex role played by the reaction parameters in controlling the size and morphology of ZIF-8 particles is still not fully understood. Even further, many individual reports propose different nucleation and growth mechanisms for ZIF-8, thus creating a fragmented view for the behavior of the system. To provide a unified view, we have generated a comprehensive data set of synthetic conditions and their final outputs and applied machine learning techniques to analyze the data. Our approach has enabled us to identify the nucleation and growth mechanisms operating for ZIF-8 in a given sub-space of synthetic variables space (chemical space) and to reveal their impact on important features such as final particle size and morphology. By doing so, we draw connections and establish a hierarchy for the role of each synthetic variable and provide with rule of thumb for attaining control on the final particle size. Our results provide a unified roadmap for the nucleation and growth mechanisms of ZIF-8 in agreement with mainstream reported trends, which can guide the rational design of ZIF-8 particles which ultimately determine their suitability for any given targeted application. Altogether, our work represents a step forward in seeking control of the properties of MOFs through a deeper understanding of the rationale behind the synthesis procedures employed for their synthesis.

Monitoring Volcanic Plumes and Clouds Using Remote Sensing: A Systematic Review

Volcanic clouds pose significant threats to air traffic, human health, and economic activity, making early detection and monitoring crucial. Accurate determination of eruptive source parameters is crucial for forecasting and implementing preventive measures. This review article aims to identify the most common remote sensing methods for monitoring volcanic clouds. To achieve this, we conducted a systematic literature review of scientific articles indexed in the Web of Science database published between 2010 and 2022, using multiple query strings across all fields. The articles were reviewed based on research topics, remote sensing methods, practical applications, case studies, and outcomes using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our study found that satellite-based remote sensing approaches are the most cost-efficient and accessible, allowing for the monitoring of volcanic clouds at various spatial scales. Brightness temperature difference is the most commonly used method for detecting volcanic clouds at a specified temperature threshold. Approaches that apply machine learning techniques help overcome the limitations of traditional methods. Despite the constraints imposed by spatial and temporal resolution and optical limitations of sensors, multiplatform approaches can overcome these limitations and improve accuracy. This study explores various techniques for monitoring volcanic clouds, identifies research gaps, and lays the foundation for future research.

An Intelligent Model and Methodology for Predicting Length of Stay and Survival in a Critical Care Hospital Unit

This paper describes the design and methodology for the development and validation of an intelligent model in the healthcare domain. The generated model relies on artificial intelligence techniques, aiming to predict the length of stay and survival rate of patients admitted to a critical care hospitalization unit with better results than predictive systems using scoring. The proposed methodology is based on the following stages: preliminary data analysis, analysis of the architecture and systems integration model, the big data model approach, information structure and process development, and the application of machine learning techniques. This investigation substantiates that automated machine learning models significantly surpass traditional prediction techniques for patient outcomes within critical care settings. Specifically, the machine learning-based model attained an F1 score of 0.351 for mortality forecast and 0.615 for length of stay, in contrast to the traditional scoring model’s F1 scores of 0.112 for mortality and 0.412 for length of stay. These results strongly support the advantages of integrating advanced computational techniques in critical healthcare environments. It is also shown that the use of integration architectures allows for improving the quality of the information by providing a data repository large enough to generate intelligent models. From a clinical point of view, obtaining more accurate results in the estimation of the ICU stay and survival offers the possibility of expanding the uses of the model to the identification and prioritization of patients who are candidates for admission to the ICU, as well as the management of patients with specific conditions.

Exploring Data Analytics in Attendance Systems: Unveiling Machine Learning Techniques, Patterns, Practices, and Emerging Trends

Purpose: The research aims to identify patterns and trends in attendance management through the application of reward and punishment systems as innovative solutions for improving employee attendance and well-being. Methods: This research utilizes a descriptive analysis approach with the application of Machine Learning (ML) techniques to enhance the accuracy of attendance pattern prediction and ML models for the classification of emerging trends and patterns. Research data were obtained through the company's attendance system and divided into two segments (80% for training and 20% for testing) while maintaining a balanced class proportion, then processed using SPSS and Python software with the Scikit-learn library. Result: The results of the study show that employee attendance is increased from 86.52% to 90.44% when the reward and punishment method is applied to the employee attendance system. Proper reward allocation can increase employee motivation to adhere to work schedules and consistently attend, while punishment tends to lead to lower attendance rates. Novelty: This research emphasizes the optimization of attendance management through data analytics approaches and the implementation of advanced technology in attendance systems with the application of ML techniques to analyze attendance data comprehensively and detect significant patterns.

Utilizing Machine Learning for Cone Penetration Test-Based Soil Classification

The cone penetration test (CPT) is widely used in geotechnical engineering to assess soil properties. Traditional methods of interpreting CPT data and classifying soils have limitations and are time-consuming. Machine learning (ML) algorithms offer a data-driven approach to automate and improve soil classification based on CPT data. In this study, the applicability of ML techniques was investigated to measure the reliability of soil classification prediction using raw CPT data. A dataset comprising raw CPT data and corresponding soil classifications derived from the adjacent boreholes was prepared for training and testing the selected ML techniques. Five ML algorithms, namely logistic regression, the support vector machine, the random forest (RF), K-nearest neighbors (KNN), and extreme gradient boosting (XGBoost), were applied. The results showed that the RF algorithm outperformed other ML methods, achieving an F1-score of 0.896. Comparing the performance of different algorithms, the RF consistently showed the best results, followed by XGBoost and KNN. These findings highlight the potential of ML algorithms, particularly the RF, in accurately predicting soil classification based on CPT data, thus improving the efficiency and reliability of geotechnical engineering applications.

Case study in machine learning for predicting moderate pressure plasma behavior

Modeling and forecasting the dynamics of complex systems, such as moderate pressure capacitively coupled plasma (CCP) systems, remains a challenge due to the interactions of physical and chemical processes across multiple scales. Historically, optimization for a given application would be accomplished via a design of experiment (DOE) study across the various external control parameters. Machine learning (ML) techniques show the potential to “forecast” process conditions not tested in a traditional DOE study and thereby allow better optimization and control of a plasma tool. In this article, we have used standard DOE as well as ML predictions to analyze I-V data in a moderate-pressure CCP system. We have demonstrated that supervised regression ML techniques can be a useful tool for extrapolating data even when a plasma system is undergoing a transition in the heating mode, in this case from the alpha to gamma mode. Classification analysis of control parameters is another possible application of ML techniques that can be deployed for system control. Here, we show that given a large set of measured data, the models can identify the gas ratio in the feed gas as well as correctly identify the operating pressure and electrode gap in almost all the cases.

Application of reinforcement learning to deduce nuclear power plant severe accident scenario

Severe accident scenarios for nuclear power plants are determined through probabilistic safety analysis (PSA). In this process, it is possible to identify the failure sequence of specific components, but assessing the impact of component failure time on the severity of accident remains a challenge. In this study, a novel approach is presented that utilizes machine learning methodologies such as reinforcement learning (RL) to complement traditional PSA. The proposed process was validated by comparing whether the most severe accident scenarios obtained through critical accident simulations can be reproduced by a RL, since this is a novel use of machine learning techniques. The comparison shows feasibility of exploring critical accident scenarios using RL. To implement the reinforcement learning methodology based on the existing system code, supervised learning model that can predict the remaining time of reactor vessel failure was implemented in this study. Based on this prediction model and data from existing catastrophic accident simulation, RL was implemented. The results obtained from RL were subsequently validated with the results of severe accident code simulation. In summary, new methodology for applying machine learning techniques to the nuclear accident analysis process was presented, and the feasibility and potential of the proposed methodology were discussed.

Bayesian network methodology and machine learning approach: an application on the impact of digital technologies on logistics service quality

PurposeThis paper aims to use Bayesian network (BN) methodology complemented by machine learning (ML) and what-if analysis to investigate the impact of digital technologies (DT) on logistics service quality (LSQ), employing the service quality (SERVQUAL) framework.Design/methodology/approachUsing a sample of 244 Italian firms, this study estimates the probability distributions associated with both DT and SERVQUAL logistics, as well as their interrelationships. Additionally, BN technique enables the application of ML techniques to uncover hidden relationships, as well as a series of what-if analyses to extract more knowledge.FindingsThe results show that the average probability of firms investing in DT for analytics (DTA) is higher than that of investing inDT for immersive experiences (DTIE). Furthermore, adopting both offers only a moderate likelihood of successfully implementing SERVQUAL logistics. Additionally, certain technologies may not directly influence some SERVQUAL dimensions. The application of ML reveals hidden relationships among technologies, enhancing the predictions of SERVQUAL logistics. Finally, what-if analyses provide further insights to guide decision-making processes aimed at enhancing SERVQUAL logistics dimensions through DTA and DTIE.Originality/valueThis research delves into the influence of DTIE and DTA on SERVQUAL logistics, thereby filling a gap in the existing literature in which no study has explored the intricate relationships between these technologies and SERVQUAL dimensions. Methodologically, we pioneer the integration of BN with ML techniques and what-if analysis, thus exploring innovative techniques to be used in logistics and supply-chain studies.

Correlation between nutrients, rare earth elements and heavy metals in O. Sativa rice plant organs by instrumentation neutron activation analysis and proton induced x-ray emission techniques

The accumulation of halogens and metals from roots to stems, leaves and grains of Oryza sativa rice plant collected in Northern Senegal (St Louis region) at the same stages of growth but at different salinity soil levels was investigated by measuring their elemental concentration by invasive instrumental techniques of neutron activation analysis and proton induced x-ray emission. The normalized elemental concentration of Na, Cl, K, (Cs, Rb, Sr, Sb, Ba Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Cu, Zn, As, Se, Br, Rb, Sb was correlated with one of rare earth elements, La, Ce, Sm, Yb, Nd, Eu, Tb, Tm, and Lu) and the one of the heavy metals Hf, Ta and Th and to assess their subsequent distribution within the Oryza sativa rice plant in a particular salinity environment. The exploration of the experimental data was performed by applying machine learning techniques with Python to find their elemental correlations. This study shows that the accumulation of iron is highly correlated with the amount of rare earth elements and heavy metals investigated. We observe an inverse correlation between Fe and Cl concentrations in the roots and stems of the Oryza sativa rice plant, in which higher levels of Fe in the roots relative to stems correspond to lower levels of Cl. We also remark that there is no accumulation of rare earth elements in the root tissues of the different Oryza sativa rice plants studied. Whereas, in the stem, leaf, and grain organs there is a strong correlation between metal contents and rare earth elements.

Medical Insurance Price Prediction Using Machine Learning

The escalating costs and complexities in the healthcare sector underscore the necessity for efficient predictive models to anticipate medical insurance prices. This study explores the application of machine learning techniques for forecasting medical insurance premiums, aiming to provide stakeholders with invaluable insights for pricing strategies and risk management. Using a comprehensive dataset encompassing demographic information, medical history, lifestyle factors, and insurance coverage details, various machine learning algorithms including regression, decision trees, random forests are employed and compared. Feature engineering techniques are applied to enhance model performance and interpretability, ensuring the inclusion of relevant predictors while mitigating overfitting. However, in recent years, the emergence of machine learning techniques has offered promising solutions to enhance medical insurance price prediction. This paper conducts an extensive review of various machine learning approaches utilized for this purpose, covering regression-based methods, time series forecasting techniques, ensemble methods, deep learning strategies, and hybrid models. We delve into the unique strengths, limitations, and practical applications of each technique. Moreover, we address the prevalent challenges associated with employing machine learning in medical insurance price prediction, such as data accessibility, feature selection, model interpretability, scalability, and generalization. Additionally, we look ahead to future research avenues and opportunities aimed at refining the accuracy and utility of machine learning models in predicting insurance prices. Through this comprehensive review, we aim to provide valuable insights for researchers, practitioners, and policymakers, facilitating informed decision-making in healthcare contexts through the utilization of machine learning methodologies.

Detecting the symptoms of Parkinson’s disease with non-standard video

BackgroundNeurodegenerative diseases, such as Parkinson’s disease (PD), necessitate frequent clinical visits and monitoring to identify changes in motor symptoms and provide appropriate care. By applying machine learning techniques to video data, automated video analysis has emerged as a promising approach to track and analyze motor symptoms, which could facilitate more timely intervention. However, existing solutions often rely on specialized equipment and recording procedures, which limits their usability in unstructured settings like the home. In this study, we developed a method to detect PD symptoms from unstructured videos of clinical assessments, without the need for specialized equipment or recording procedures.MethodsTwenty-eight individuals with Parkinson’s disease completed a video-recorded motor examination that included the finger-to-nose and hand pronation-supination tasks. Clinical staff provided ground truth scores for the level of Parkinsonian symptoms present. For each video, we used a pre-existing model called PIXIE to measure the location of several joints on the person’s body and quantify how they were moving. Features derived from the joint angles and trajectories, designed to be robust to recording angle, were then used to train two types of machine-learning classifiers (random forests and support vector machines) to detect the presence of PD symptoms.ResultsThe support vector machine trained on the finger-to-nose task had an F1 score of 0.93 while the random forest trained on the same task yielded an F1 score of 0.85. The support vector machine and random forest trained on the hand pronation-supination task had F1 scores of 0.20 and 0.33, respectively.ConclusionThese results demonstrate the feasibility of developing video analysis tools to track motor symptoms across variable perspectives. These tools do not work equally well for all tasks, however. This technology has the potential to overcome barriers to access for many individuals with degenerative neurological diseases like PD, providing them with a more convenient and timely method to monitor symptom progression, without requiring a structured video recording procedure. Ultimately, more frequent and objective home assessments of motor function could enable more precise telehealth optimization of interventions to improve clinical outcomes inside and outside of the clinic.

Application of Machine Learning Techniques for the Diagnosis of Obstructive Sleep Apnea/Hypopnea Syndrome.

Obstructive sleep apnea/hypopnea syndrome (OSAHS) is a condition linked to severe cardiovascular and neuropsychological consequences, characterized by recurrent episodes of partial or complete upper airway obstruction during sleep, leading to compromised ventilation, hypoxemia, and micro-arousals. Polysomnography (PSG) serves as the gold standard for confirming OSAHS, yet its extended duration, high cost, and limited availability pose significant challenges. In this paper, we employ a range of machine learning techniques, including Neural Networks, Decision Trees, Random Forests, and Extra Trees, for OSAHS diagnosis. This approach aims to achieve a diagnostic process that is not only more accessible but also more efficient. The dataset utilized in this study consists of records from 601 adults assessed between 2014 and 2016 at a specialized sleep medical center in Colombia. This research underscores the efficacy of ensemble methods, specifically Random Forests and Extra Trees, achieving an area under the Receiver Operating Characteristic (ROC) curve of 89.2% and 89.6%, respectively. Additionally, a web application has been devised, integrating the optimal model, empowering qualified medical practitioners to make informed decisions through patient registration, an input of 18 variables, and the utilization of the Random Forests model for OSAHS screening.

Machine learning applications in studies of the physical properties of active galactic nuclei based on photometric observations

Context. We investigate the physical nature of active galactic nuclei (AGNs) using machine learning (ML) tools. Aims. We show that the redshift, z, bolometric luminosity, LBol, central mass of the supermassive black hole (SMBH), MBH, Eddington ratio, λEdd, and AGN class (obscured or unobscured) can be reconstructed through multi-wavelength photometric observations only. Methods. We trained a random forest regressor (RFR) ML-model on 7616 spectroscopically observed AGNs from the SPIDERS-AGN survey, which had previously been cross-matched with soft X-ray observations (from ROSAT or XMM), WISE mid-infrared photometry, and optical photometry from SDSS ugriz filters. We built a catalog of 21 050 AGNs that were subsequently reconstructed with the trained RFR; for 9687 sources, we found archival redshift measurements. All AGNs were classified as either type 1 or type 2 using a random forest classifier (RFC) algorithm on a subset of known sources. All known photometric measurement uncertainties were incorporated via a simulation-based approach. Results. We present the reconstructed catalog of 21 050 AGNs with redshifts ranging from 0 < z < 2.5. We determined z estimations for 11 363 new sources, with both accuracy and outlier rates within 2%. The distinction between type 1 or type 2 AGNs could be identified with respective efficiencies of 94% and 89%. The estimated obscuration level, a proxy for AGN classification, of all sources is given in the dataset. The LBol, MBH, and λEdd values are given for 21 050 new sources with their estimated error. These results have been made publicly available. Conclusions. The release of this catalog will advance AGN studies by presenting key parameters of the accretion history of 6 dex in luminosity over a wide range of z. Similar applications of ML techniques using photometric data only will be essential in the future, with large datasets from eROSITA, JSWT, and the VRO poised to be released in the next decade.

Correlations and impact of anaerobic digestion operating parameters on the start-up duration: Database construction for robust start-up guidelines

Anaerobic digestion (AD) has become a popular technique for organic waste management while offering economic and environmental advantages. As AD becomes increasingly prevalent worldwide, research efforts are primarily focused on optimizing its processes. During the operation of AD systems, the occurrence of unstable events is inevitable. So far, numerous conclusions have been drawn from full and lab-scale studies regarding the driving factors of start-up perturbations. However, the lack of standardized practices reported in start-up studies raises concerns about the comparability and reliability of obtained data. This study aims to develop a knowledge database and investigate the possibility of applying machine learning techniques on experimentation-extracted data to assist start-up planning and monitoring. Thus, a standardized database referencing 75 cases of start-up of one-stage wet continuously-stirred tank reactors (CSTR) processing agricultural, industrial, or municipal organic effluent in mono-digestion from 31 studies was constructed. 10 % of the total observations included in this database concern failed start-up experiments. Then, correlations between the parameters and their impacts on the start-up duration were studied using multivariate analysis and a model-based ranking methodology. Insights into trends of choices were highlighted through the correlation analysis of the database. As such, scenarios favoring short start-up duration were found to involve relatively low retention times (average initial and final hydraulic retention times, (HRTi) and (HRTf) of 26.25 and 20.6 days, respectively), high mean organic loading rates (average OLRmean of 5.24 g VS·d−1·L −1) and the processing of highly fermentable substrates (average feed volatile solids (VSfeed) of 81.35 g L−1). The model-based ranking of AD parameters demonstrated that the HRTf, the VSfeed, and the target temperature (Tf) have the strongest impact on the start-up duration, receiving the highest relative scores among the evaluated AD parameters. The database could serve as a reference for comparison purposes of future start-up studies allowing the identification of factors that should be closely controlled.

Towards Early Intervention: Detecting Parkinson's Disease through Voice Analysis with Machine Learning

Introduction/ Background This study aims to utilize machine learning algorithms for early detection of Parkinson's Disease (PD) through voice recording analysis. Employing advanced machine learning techniques and a comprehensive dataset of voice samples, the objective is to develop a non-invasive, accurate, and reliable method for PD diagnosis, contributing to early intervention and management of the disease. Parkinson's Disease (PD) is a prevalent neurodegenerative disorder impacting millions globally. Early and accurate diagnosis is crucial for effective management and treatment. This study leverages Machine Learning (ML) algorithms to analyze voice recordings, aiming to improve PD detection. Materials and Methods We utilized a dataset of 195 voice samples with 23 attributes, applying machine learning algorithms such as Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Convolutional Neural Networks (CNN). The dataset was preprocessed, balanced, and evaluated using various performance metrics. Results The K-Nearest Neighbors (KNN) model demonstrated superior performance, achieving high precision (0.96-1.00), recall (0.97-1.00), and F1-scores (0.98-0.99) for both PD and non-PD classes, demonstrating an overall accuracy of 0.98 across 59 samples. This showcases its effectiveness in PD detection via voice analysis. Discussion This research underscores the potential of ML in revolutionizing PD detection through non-invasive methods. By comparing various algorithms, the study not only identifies the most effective model but also contributes to the broader understanding of applying ML techniques in healthcare. Conclusion The study's findings advocate for the KNN model as a promising tool for early and accurate PD diagnosis through voice analysis. The success of this model opens avenues for future research, including the exploration of more advanced algorithms and the integration of these models into practical diagnostic applications.

ENHANCING BRIX VALUE PREDICTION IN STRAWBERRIES USING MACHINE LEARNING: A FUSION OF PHYSIOCHEMICAL AND COLOR-BASED FEATURES FOR IMPROVED SWEETNESS ASSESSMENT

This study contributes to the ongoing wave of artificial intelligence integration by applying machine learning techniques to automate the assessment of strawberry quality. This research focuses on determining if the sweetness of strawberries can be predicted using a combination of physiochemical variables, their interaction parameters, and color-based features extracted from image data. This research used a 150-sample collection of strawberry images and physiochemical characteristics such as salinity, specific gravity, pH, and Brix. Normalized raw and derived feature variables and selected dataset transformations were done. We then split the dataset into mutual exclusivity training and test sets. Exponential Gaussian Process Regression (GPR) suited well due to low validation errors. This best model predicted Brix values for the remaining test samples. The Mean Absolute Percentage Error(MAPE) showed 98.783% forecast accuracy (Acc). We also examined the model's coefficient of determination (R2) values, which were 0.78 and 0.9739 for training and testing, respectively. The Mean Square Error (MSE) and Mean Absolute Error (MAE) obtained after training were 0.32994 and 0.0453, and testing was 0.35286 and 0.0663. Using input characteristics with high Acc and low error rates, deep learning models like Recurrent Neural Network (RNN) and its derivatives were constructed. Using physiochemical and visual data, machine learning and deep learning models successfully predict strawberry sweetness. This prediction accuracy shows the complex link between internal components and Brix readings, enabling high-quality strawberry production.

A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects

This paper focuses on the current application of machine learning (ML) in enhanced oil recovery (EOR) through CO2 injection, which exhibits promising economic and environmental benefits for climate-change mitigation strategies. Our comprehensive review explores the diverse use cases of ML techniques in CO2-EOR, including aspects such as minimum miscible pressure (MMP) prediction, well location optimization, oil production and recovery factor prediction, multi-objective optimization, Pressure–Volume–Temperature (PVT) property estimation, Water Alternating Gas (WAG) analysis, and CO2-foam EOR, from 101 reviewed papers. We catalog relative information, including the input parameters, objectives, data sources, train/test/validate information, results, evaluation, and rating score for each area based on criteria such as data quality, ML-building process, and the analysis of results. We also briefly summarized the benefits and limitations of ML methods in petroleum industry applications. Our detailed and extensive study could serve as an invaluable reference for employing ML techniques in the petroleum industry. Based on the review, we found that ML techniques offer great potential in solving problems in the majority of CO2-EOR areas involving prediction and regression. With the generation of massive amounts of data in the everyday oil and gas industry, machine learning techniques can provide efficient and reliable preliminary results for the industry.

Reinforcement learning-based SDN routing scheme empowered by causality detection and GNN.

In recent years, with the rapid development of network applications and the increasing demand for high-quality network service, quality-of-service (QoS) routing has emerged as a critical network technology. The application of machine learning techniques, particularly reinforcement learning and graph neural network, has garnered significant attention in addressing this problem. However, existing reinforcement learning methods lack research on the causal impact of agent actions on the interactive environment, and graph neural network fail to effectively represent link features, which are pivotal for routing optimization. Therefore, this study quantifies the causal influence between the intelligent agent and the interactive environment based on causal inference techniques, aiming to guide the intelligent agent in improving the efficiency of exploring the action space. Simultaneously, graph neural network is employed to embed node and link features, and a reward function is designed that comprehensively considers network performance metrics and causality relevance. A centralized reinforcement learning method is proposed to effectively achieve QoS-aware routing in Software-Defined Networking (SDN). Finally, experiments are conducted in a network simulation environment, and metrics such as packet loss, delay, and throughput all outperform the baseline.

Volcanic clouds detection applying machine learning techniques to GNSS radio occultations

Volcanic clouds detection is a challenge especially when meteorological clouds are present in the same area. Several algorithms have been developed to detect and monitor volcanic clouds by using satellite instruments based on different remote sensing techniques. This work aims at classifying volcanic clouds based on atmospheric profiles retrieved by the GNSS (Global Navigation Satellite Systems) radio occultation technique. We collocated the radio occultations with the volcanic cloud detection from AIRS (Atmospheric InfraRed Sounder) and IASI (Infrared Atmospheric Sounding Interferometer) for 11 big eruptions happening in the period 2008–2015 resulting in about 15000 profiles. We created an archive with the collocations and a corresponding number of profiles in “non-volcanic” environment in the same area and on the same period of the year. A support vector machine algorithm was applied to the archive in order to classify the clouds and to distinguish the volcanic clouds from the other types. The model performances are promising: the GNSS radio occultations are able to distinguish the volcanic clouds with an accuracy higher than 80% when the eruption occurs at high latitudes. The performances of the model are affected by the number of collocations used for the training. Nowadays, the number of radio occultations is higher than in the period considered in this research, making this work a pioneering study for a future operational product.

Stock Market Analysis Using Machine Learning

The stock market is a dynamic and complex financial system influenced by numerous factors, making accurate forecasting a challenging task. Traditional methods often fall short in capturing the intricate patterns within financial data. This research paper explores the application of machine learning techniques for stock market forecasting, aiming to provide a comprehensive overview of the current state of the field. The paper reviews relevant literature, discusses popular machine learning algorithms, and assesses their effectiveness in predicting stock market trends. Additionally, real-world applications and challenges in implementing machine learning for stock market forecasting are explored.