Machine Learning-based Analysis Research Articles

Protein Adsorption on Solid Surfaces: Data Mining, Database, Molecular Surface-Derived Properties, and Semiempirical Relationships.

Protein adsorption on solid surfaces is a process relevant to biological, medical, industrial, and environmental applications. Despite this wide interest and advancement in measurement techniques, the complexity of protein adsorption has frustrated its accurate prediction. To address this challenge, here, data regarding protein adsorption reported in the last four decades was collected, checked for completeness and correctness, organized, and archived in an upgraded, freely accessible Biomolecular Adsorption Database, which is equivalent to a large-scale, ad hoc, crowd-sourced multifactorial experiment. The shape and physicochemical properties of the proteins present in the database were quantified on their molecular surfaces using an in-house program (ProMS) operating as an add-on to the PyMol software. Machine learning-based analysis indicated that protein adsorption on hydrophobic and hydrophilic surfaces is modulated by different sets of operational, structural, and molecular surface-based physicochemical parameters. Separately, the adsorption data regarding four "benchmark" proteins, i.e., lysozyme, albumin, IgG, and fibrinogen, was processed by piecewise linear regression with the protein monolayer acting as breakpoint, using the linearization of the Langmuir isotherm formalism, resulting in semiempirical relationships predicting protein adsorption. These relationships, derived separately for hydrophilic and hydrophobic surfaces, described well the protein concentration on the surface as a function of the protein concentration in solution, adsorbing surface contact angle, ionic strength, pH, and temperature of the carrying fluid, and the difference between pH and the isoelectric point of the protein. When applying the semiempirical relationships derived for benchmark proteins to two other "test" proteins with known PDB structure, i.e., β-lactoglobulin and α-lactalbumin, the errors of this extrapolation were found to be in a linear relationship with the dissimilarity between the benchmark and the test proteins. The work presented here can be used for the estimation of operational parameters modulating protein adsorption for various applications such as diagnostic devices, pharmaceuticals, biomaterials, or the food industry.

Gradient boosted regression as a tool to reveal key drivers of temporal dynamics in a synthetic yeast community.

Microbial communities are vital to our lives, yet their ecological functioning and dynamics remain poorly understood. This understanding is crucial for assessing threats to these systems and leveraging their biotechnological applications. Given that temporal dynamics are linked to community functioning, this study investigated the drivers of community succession in the wine yeast community. We experimentally generated population dynamics data and used it to create an interpretable model with a gradient-boosted regression tree approach. The model was trained on temporal data of viable species populations in various combinations, including pairs, triplets, and quadruplets, and was evaluated for predictive accuracy and input feature importance. Key findings revealed that the inoculation dosage of non-Saccharomyces species significantly influences their performance in mixed cultures, while Saccharomyces cerevisiae consistently dominates regardless of initial abundance. Additionally, we observed multispecies interactions where the dynamics of Wickerhamomyces anomalus were influenced by Torulaspora delbrueckii in pairwise cultures, but this interaction was altered by the inclusion of S. cerevisiae. This study provides insights into yeast community succession and offers valuable machine learning-based analysis techniques applicable to other microbial communities, opening new avenues for harnessing microbial communities.

The biomedical knowledge graph of symptom phenotype in coronary artery plaque: machine learning-based analysis of real-world clinical data.

A knowledge graph can effectively showcase the essential characteristics of data and is increasingly emerging as a significant means of integrating information in the field of artificial intelligence. Coronary artery plaque represents a significant etiology of cardiovascular events, posing a diagnostic challenge for clinicians who are confronted with a multitude of nonspecific symptoms. To visualize the hierarchical relationship network graph of the molecular mechanisms underlying plaque properties and symptom phenotypes, patient symptomatology was extracted from electronic health record data from real-world clinical settings. Phenotypic networks were constructed utilizing clinical data and protein‒protein interaction networks. Machine learning techniques, including convolutional neural networks, Dijkstra's algorithm, and gene ontology semantic similarity, were employed to quantify clinical and biological features within the network. The resulting features were then utilized to train a K-nearest neighbor model, yielding 23 symptoms, 41 association rules, and 61 hub genes across the three types of plaques studied, achieving an area under the curve of 92.5%. Weighted correlation network analysis and pathway enrichment were subsequently utilized to identify lipid status-related genes and inflammation-associated pathways that could help explain the differences in plaque properties. To confirm the validity of the network graph model, we conducted coexpression analysis of the hub genes to evaluate their potential diagnostic value. Additionally, we investigated immune cell infiltration, examined the correlations between hub genes and immune cells, and validated the reliability of the identified biological pathways. By integrating clinical data and molecular network information, this biomedical knowledge graph model effectively elucidated the potential molecular mechanisms that collude symptoms, diseases, and molecules.

An assessment of nanotechnology-based interventions for cleaning up toxic heavy metal/metalloid-contaminated agroecosystems: Potentials and issues

Heavy metals (HMs) are among the most dangerous environmental variables for a variety of life forms, including crops. Accumulation of HMs in consumables and their subsequent transmission to the food web are serious concerns for scientific communities and policy makers. The function of essential plant cellular macromolecules is substantially hampered by HMs, which eventually have a detrimental effect on agricultural yield. Among these HMs, three were considered, i.e., arsenic, cadmium, and chromium, in this review, from agro-ecosystem perspective. Compared with conventional plant growth regulators, the use of nanoparticles (NPs) is a relatively recent, successful, and promising method among the many methods employed to address or alleviate the toxicity of HMs. The ability of NPs to reduce HM mobility in soil, reduce HM availability, enhance the ability of the apoplastic barrier to prevent HM translocation inside the plant, strengthen the plant's antioxidant system by significantly enhancing the activities of many enzymatic and nonenzymatic antioxidants, and increase the generation of specialized metabolites together support the effectiveness of NPs as stress relievers. In this review article, to assess the efficacy of various NP types in ameliorating HM toxicity in plants, we adopted a ‘fusion approach’, in which a machine learning-based analysis was used to systematically highlight current research trends based on which an extensive literature survey is planned. A holistic assessment of HMs and NMs was subsequently carried out to highlight the future course of action(s).

ARBUR, a machine learning-based analysis system for relating behaviors and ultrasonic vocalizations of rats

ARBUR, a machine learning-based analysis system for relating behaviors and ultrasonic vocalizations of rats

A Predictive Machine Learning-Based Analysis for In-Tube Co-current Steam Condensation Heat Transfer in the Presence of Non-condensable Gases

AbstractThe primary objective of this study is to develop a predictive model that accurately estimates the condensation heat transfer coefficient (HTC) in the context of in-tube co-current flow condensation, with specific consideration given to the influence of non-condensable gases (NCGs). The need to find useful models and/or correlations that can accurately predict how well a passive condenser in the Economic Simplified Boiling Water Reactor will transfer heat during condensation is greatly warranted. This innovative cooling system utilizes gravity to condense water vapor, effectively dissipating heat from the containment vessel in the event of an accident and transferring the heat into the secondary cooling system. For the development of the forecasting model, we employed Multi-Gens Genetic Programming implemented in MATLAB. This approach possesses the capability of enabling visualization and articulating a transparent mathematical expression that directly establishes the connection between inputs and outputs. This model was specifically developed for forecasting the HTC during the condensation process, considering two distinct types of NCG: air, helium, and a mixture of air and helium, in addition to data for pure steam. The model was trained using a dataset comprising 2913 data points gathered from five experimental references. The model's input comprises a varied range of parameters, including local location ($$z$$ z ), temperature of the steam/NCG mixture ($${T}_{mix}$$ T mix ), condenser wall temperature ($${T}_{wall}$$ T wall ), coolant flow rate ($$\beta_{c}$$ β c ), total pressure ($${P}_{tot}$$ P tot ), condenser inner diameter ($${D}_{1}$$ D 1 ), condenser outer diameter ($${D}_{2}$$ D 2 ), molecular weight (MM), NCG mass fraction ($${w}_{NCG}$$ w NCG ), and mixture Reynolds number ($${Re}_{mix}$$ Re mix ). The output of our model corresponds to the condensation HTC ($$h$$ h ). Four specific correlations were examined with the third correlation attaining the highest anticipated values within a deviation range of ± 30, reaching 90.9%, exhibiting good performance in terms of both performance index and coefficient of determination. This correlation was validated, and its accuracy assessed by employing an unused training dataset that included nitrogen as a NCG. The outcomes showed that 92.94% of the predicted data fell within a deviation range of ± 30%. The findings of this study signify a notable advancement in the analysis of datasets obtained from experiments. Particularly, it delves into the impact of NCGs by considering their molecular weight and mass fraction.

Integration into 3D Printing for Image Processing using AI ML

The challenges of physical measurement for prosthetic development and highlight how our integrated tool offers a remote solution to address these challenges, promoting accessibility, convenience, and active participation in the prosthetic design process. Our Measurement Module for Prosthetic Hand Dimensions is a comparison of Image Processing and Distance Measurement Methods The creation of a module to precisely measure finger dimensions for the creation of prosthetic hands is the main goal of this research project. The module provides distance measurement and image-based input as two different ways to get dimensions. Users place their hands at predetermined distances from a screen in the distance measurement option, while users submit photographs of their hands with reference objects for scale in the image based input option. In order To provide a proper fit and functionality, the collected dimensions are an essential component of the prosthetic hand construction process. The study contrasts the efficiency of distance measurement methods employing cameras or other cameras with image processing techniques like edge recognition and contour analysis. Accuracy, usability, and efficiency are among the factors that are assessed to identify the best method for acquiring dimensions. OpenCV for image processing, TensorFlow for machine learning-based analysis, Tkinter for UI design, Mediapipe for landmark identification and hand tracking, and NumPy for numerical operations are just a few of the open-source libraries that are used in this module. The module's possible influence on improving the creation and customization of prosthetic hands is also covered. By offering insights into effective and precise dimension measurement methodologies for customized prosthetic hand design, the study's findings advance the area of prosthetics

Deep Machine Learning-based Analysis for Intelligent Phonetic Language Recognition

Modern speech generating systems can produce results that are almost as visually realistic as actual sounds. They still require further production management. This research presents a paradigm for managing prosodic output using explicit, unambiguous, and understandable parameters. We utilize this strategy to emphasize key words and provide a variety of architectural possibilities based on a richness of labelled resources. In an objective voice, we compare the options for producing data with or without labels. We assess them using listening tests that demonstrate our ability to retain the same level of naturalness while effectively attaining regulated concentration over a specific area.

Validation of Machine Learning-Based Assessment of Major Depressive Disorder from Paralinguistic Speech Characteristics in Routine Care

New developments in machine learning-based analysis of speech can be hypothesized to facilitate the long-term monitoring of major depressive disorder (MDD) during and after treatment. To test this hypothesis, we collected 550 speech samples from telephone-based clinical interviews with 267 individuals in routine care. With this data, we trained and evaluated a machine learning system to identify the absence/presence of a MDD diagnosis (as assessed with the Structured Clinical Interview for DSM-IV) from paralinguistic speech characteristics. Our system classified diagnostic status of MDD with an accuracy of 66% (sensitivity: 70%, specificity: 62%). Permutation tests indicated that the machine learning system classified MDD significantly better than chance. However, deriving diagnoses from cut-off scores of common depression scales was superior to the machine learning system with an accuracy of 73% for the Hamilton Rating Scale for Depression (HRSD), 74% for the Quick Inventory of Depressive Symptomatology–Clinician version (QIDS-C), and 73% for the depression module of the Patient Health Questionnaire (PHQ-9). Moreover, training a machine learning system that incorporated both speech analysis and depression scales resulted in accuracies between 73 and 76%. Thus, while findings of the present study demonstrate that automated speech analysis shows the potential of identifying patterns of depressed speech, it does not substantially improve the validity of classifications from common depression scales. In conclusion, speech analysis may not yet be able to replace common depression scales in clinical practice, since it cannot yet provide the necessary accuracy in depression detection. This trial is registered with DRKS00023670.

Machine Learning-Based Analysis Reveals Triterpene Saponins and Their Aglycones in Cimicifuga racemosa as Critical Mediators of AMPK Activation.

Cimicifuga racemosa (CR) extracts contain diverse constituents such as saponins. These saponins, which act as a defense against herbivores and pathogens also show promise in treating human conditions such as heart failure, pain, hypercholesterolemia, cancer, and inflammation. Some of these effects are mediated by activating AMP-dependent protein kinase (AMPK). Therefore, comprehensive screening for activating constituents in a CR extract is highly desirable. Employing machine learning (ML) techniques such as Deep Neural Networks (DNN), Logistic Regression Classification (LRC), and Random Forest Classification (RFC) with molecular fingerprint MACCS descriptors, 95 CR constituents were classified. Calibration involved 50 randomly chosen positive and negative controls. LRC achieved the highest overall test accuracy (90.2%), but DNN and RFC surpassed it in precision, sensitivity, specificity, and ROC AUC. All CR constituents were predicted as activators, except for three non-triterpene compounds. The validity of these classifications was supported by good calibration, with misclassifications ranging from 3% to 17% across the various models. High sensitivity (84.5-87.2%) and specificity (84.1-91.4%) suggest suitability for screening. The results demonstrate the potential of triterpene saponins and aglycones in activating AMP-dependent protein kinase (AMPK), providing the rationale for further clinical exploration of CR extracts in metabolic pathway-related conditions.

Prediction of dysphagia aspiration through machine learning-based analysis of patients’ postprandial voices

BackgroundConventional diagnostic methods for dysphagia have limitations such as long wait times, radiation risks, and restricted evaluation. Therefore, voice-based diagnostic and monitoring technologies are required to overcome these limitations. Based on our hypothesis regarding the impact of weakened muscle strength and the presence of aspiration on vocal characteristics, this single-center, prospective study aimed to develop a machine-learning algorithm for predicting dysphagia status (normal, and aspiration) by analyzing postprandial voice limiting intake to 3 cc.MethodsConducted from September 2021 to February 2023 at Seoul National University Bundang Hospital, this single center, prospective cohort study included 198 participants aged 40 or older, with 128 without suspected dysphagia and 70 with dysphagia-aspiration. Voice data from participants were collected and used to develop dysphagia prediction models using the Multi-Layer Perceptron (MLP) with MobileNet V3. Male-only, female-only, and combined models were constructed using 10-fold cross-validation. Through the inference process, we established a model capable of probabilistically categorizing a new patient's voice as either normal or indicating the possibility of aspiration.ResultsThe pre-trained models (mn40_as and mn30_as) exhibited superior performance compared to the non-pre-trained models (mn4.0 and mn3.0). Overall, the best-performing model, mn30_as, which is a pre-trained model, demonstrated an average AUC across 10 folds as follows: combined model 0.8361 (95% CI 0.7667–0.9056; max 0.9541), male model 0.8010 (95% CI 0.6589–0.9432; max 1.000), and female model 0.7572 (95% CI 0.6578–0.8567; max 0.9779). However, for the female model, a slightly higher result was observed with the mn4.0, which scored 0.7679 (95% CI 0.6426–0.8931; max 0.9722). Additionally, the other models (pre-trained; mn40_as, non-pre-trained; mn4.0 and mn3.0) also achieved performance above 0.7 in most cases, and the highest fold-level performance for most models was approximately around 0.9. The ‘mn’ in model names refers to MobileNet and the following number indicates the ‘width_mult’ parameter.ConclusionsIn this study, we used mel-spectrogram analysis and a MobileNetV3 model for predicting dysphagia aspiration. Our research highlights voice analysis potential in dysphagia screening, diagnosis, and monitoring, aiming for non-invasive safer, and more effective interventions.Trial registration: This study was approved by the IRB (No. B-2109-707-303) and registered on clinicaltrials.gov (ID: NCT05149976).

Machine learning-based analysis for prediction of surgical necrotizing enterocolitis in very low birth weight infants using perinatal factors: a nationwide cohort study.

Early prediction of surgical necrotizing enterocolitis (sNEC) in preterm infants is important. However, owing to the complexity of the disease, identifying infants with NEC at a high risk for surgical intervention is difficult. We developed a machine learning (ML) algorithm to predict sNEC using perinatal factors obtained from the national cohort registry of very low birth weight (VLBW) infants. Data were collected from the medical records of 16,385 VLBW infants registered in the Korean Neonatal Network (KNN). Infants who underwent surgical intervention were identified with sNEC, and infants who received medical treatment, with medical NEC (mNEC). We used 38 variables, including maternal, prenatal, and postnatal factors that were obtained within 1week of birth, for training. A total of 1085 patients had NEC (654 with sNEC and 431 with mNEC). VLBW infants showed a higher incidence of sNEC at a lower gestational age (GA) (p < 0.001). Our proposed ensemble model showed an area under the receiver operating characteristic curve of 0.721 for sNEC prediction. Conclusion: Proposed ensemble model may help predict which infants with NEC are likely to develop sNEC. Through early prediction and prompt intervention, prognosis of sNEC may be improved. What is Known: • Machine learning (ML)-based techniques have been employed in NEC research for prediction, diagnosis, and prognosis, with promising outcomes. • While most studies have utilized abdominal radiographs and clinical manifestations of NEC as data sources, and have demonstrated their usefulness, they may prove weak in terms of early prediction. What is New: • We analyzed the perinatal factors of VLBW infants acquired within 7 days of birth and used ML-based analysis to identify which infants with NEC are vulnerable to clinical deterioration and at high risk for surgical intervention using nationwide cohort data.

Machine Learning-based Analysis of Publications Funded by the National Institutes of Health's Initial COVID-19 Pandemic Response.

The National Institutes of Health (NIH) mobilized more than $4 billion in extramural funding for the COVID-19 pandemic. Assessing the research output from this effort is crucial to understanding how the scientific community leveraged federal funding and responded to this public health crisis. NIH-funded COVID-19 grants awarded between January 2020 and December 2021 were identified from NIH Research Portfolio Online Reporting Tools Expenditures and Results using the "COVID-19 Response" filter. PubMed identifications of publications under these grants were collected and the NIH iCite tool was used to determine citation counts and focus (eg, clinical, animal). iCite and the NIH's LitCOVID database were used to identify publications directly related to COVID-19. Publication titles and Medical Subject Heading terms were used as inputs to a machine learning-based model built to identify common topics/themes within the publications. We evaluated 2401 grants that resulted in 14 654 publications. The majority of these papers were published in peer-reviewed journals, though 483 were published to preprint servers. In total, 2764 (19%) papers were directly related to COVID-19 and generated 252 029 citations. These papers were mostly clinically focused (62%), followed by cell/molecular (32%), and animal focused (6%). Roughly 60% of preprint publications were cell/molecular-focused, compared with 26% of nonpreprint publications. The machine learning-based model identified the top 3 research topics to be clinical trials and outcomes research (8.5% of papers), coronavirus-related heart and lung damage (7.3%), and COVID-19 transmission/epidemiology (7.2%). This study provides key insights regarding how researchers leveraged federal funding to study the COVID-19 pandemic during its initial phase.

Machine learning-based analysis of 68Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade.

Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications and facilitate treatment management. We aim to propose a machine-learning model for tumor grade estimation based on 68Ga-PSMA-11 PET/CT images in prostate cancer patients. This study included 90 eligible participants out of 244 biopsy-proven prostate cancer patients who underwent staging 68Ga-PSMA-11 PET/CT imaging. The patients were divided into high and low-intermediate groups based on their Gleason scores. The PET-only images were manually segmented, both lesion-based and whole prostate, by two experienced nuclear medicine physicians. Four feature selection algorithms and five classifiers were applied to Combat-harmonized and non-harmonized datasets. To evaluate the model's generalizability across different institutions, we performed leave-one-center-out cross-validation (LOOCV). The metrics derived from the receiver operating characteristic curve were used to assess model performance. In the whole prostate segmentation, combining the ANOVA algorithm as the feature selector withRandom Forest (RF) and Extra Trees (ET) classifiers resulted in the highest performance among the models, with an AUC of 0.78 and 083, respectively. In the lesion-based segmentation, the highest AUC was achieved byMRMR feature selector + Linear Discriminant Analysis (LDA) andLogistic Regression (LR) classifiers (0.76 and 0.79, respectively). The LOOCV results revealed that both the RF_ANOVA and ET_ANOVA models showed high levels of accuracy and generalizability across different centers, with an average AUC value of 0.87 for the ET_ANOVA combination. Machine learning-based analysis of radiomics features extracted from 68Ga-PSMA-11 PET/CT scans can accurately classify prostate tumors into low-risk and intermediate- to high-risk groups.

Machine learning-based analysis of adverse events in mesh implant surgery reports

AbstractMesh implant surgery, commonly used for various medical conditions, has been linked to a range of negative effects, significantly impacting patient experiences and outcomes. Additionally, the growing volume of medical data, especially text-based reports, presents challenges in deriving meaningful insights for informed healthcare decisions. To address these challenges, our study extensively analyzed the Manufacturer and User Facility Device Experience (MAUDE) dataset from 2000 to 2021. We have meticulously identified a range of adverse events associated with mesh implant surgeries, including Dyspareunia, Urinary problems, Chronic Inflammation, Prolapse Recurrence, Mesh Erosion, Urinary Tract Infections, discomfort, and sleep disturbances. Using topic modeling, we explored patient experiences and the interrelationships among these adverse events. This approach uncovered key topics linked to mesh surgery, such as Stress Urinary Incontinence, Incisional Hernia, Inguinal Hernia, and Umbilical Hernia, along with their side effects. While the analysis focused on common symptoms such as pain, infection, and bleeding, it also brought to light specific symptoms like sleeping issues, mental stress, and discomfort. We also examined the interconnectedness of these adverse events with identified topics and their temporal trends, revealing shifts in patient experiences over time. Notably, there was an increase in reports of Stress Urinary Incontinence around 2011–2012 and a surge in Inguinal Hernia concerns in 2017–2018. This study provides a comprehensive understanding of adverse events and associated topics in mesh implant surgeries, contributing valuable insights into patient experiences and aiding in informed healthcare decision-making.

Machine Learning-Based Analysis of Academic Performance Determinants in Somaliland: Insights from the 2021/2022 National Secondary School Exams

Machine Learning-Based Analysis of Academic Performance Determinants in Somaliland: Insights from the 2021/2022 National Secondary School Exams

Machine learning–based analysis of non-invasive measurements for predicting intracardiac pressures

Abstract Aims Early detection of congestion has demonstrated to improve outcomes in heart failure (HF) patients. However, there is limited access to invasively haemodynamic parameters to guide treatment. This study aims to develop a model to estimate the invasively measured pulmonary capillary wedge pressure (PCWP) using non-invasive measurements with both traditional statistics and machine learning (ML) techniques. Methods and results The study involved patients undergoing right-sided heart catheterization at Erasmus MC, Rotterdam, from 2017 to 2022. Invasively measured PCWP served as outcomes. Model features included non-invasive measurements of arterial blood pressure, saturation, heart rate (variability), weight, and temperature. Various traditional and ML techniques were used, and performance was assessed using R2 and area under the curve (AUC) for regression and classification models, respectively. A total of 853 procedures were included, of which 31% had HF as primary diagnosis and 49% had a PCWP of 12 mmHg or higher. The mean age of the cohort was 59 ± 14 years, and 52% were male. The heart rate variability had the highest correlation with the PCWP with a correlation of 0.16. All the regression models resulted in low R2 values of up to 0.04, and the classification models resulted in AUC values of up to 0.59. Conclusion In this study, non-invasive methods, both traditional and ML-based, showed limited correlation to PCWP. This highlights the weak correlation between traditional HF monitoring and haemodynamic parameters, also emphasizing the limitations of single non-invasive measurements. Future research should explore trend analysis and additional features to improve non-invasive haemodynamic monitoring, as there is a clear demand for further advancements in this field.

Qualitative classification of thrombus images as a way to improve quantitative analysis of thrombus formation in flow chamber assays.

Thrombus formation in vitro under flow conditions is one of the most widely used methods to study haemostasis and to evaluate the activity of potential antithrombotic compounds. Assessment of the results of these experiments is often based on a quantification of microscopic images of thrombi. In a majority of reported analysis all thrombi visualised in an image are quantified as one homogenous class. In some protocols, qualitative assessment of thrombi morphology based on a visual comparison of evaluated images with representative images of predefined classes of thrombi are performed by experienced analysts. In presented paper we show how the quantitative analysis can be improved by classification of thrombi on the basis of defined morphological features prior to quantification and we suggest that machine learning-based approach can improve this way of analysis. We tested the applicability of machine learning-based segmentation and classification of thrombi images to improve the outcome of quantification of the results of flow chamber assays. For this, we used the public domain machine learning software Ilastik for bioimage analysis developed at the European Molecular Biology Laboratory. A model was trained to distinguish two classes of thrombi based on certain morphological features which apparently correspond to the stage of thrombus development. Thrombi formed in the presence of a model antiplatelet compound-abciximab or in control conditions were quantified with the use of this model and the results were compared to quantification where all thrombi were quantified as a homogenous class. Machine learning-based analysis was capable of effective distinguishing of two classes of morphologically distinct platelet aggregates. The use of the model which segmented and quantified only the objects recognized as compacted structures provided results which better mirrored the actual effect of an antiplatelet treatment than quantification based on all structures. Classification of thrombi enabled by machine learning increases the relevance of quantitative information and allows better evaluation of the results of in vitro thrombosis assays.

Evaluation of Machine Learning Techniques for Enhancing Scholarship Schemes Using Artificial Emotional Intelligence

This paper investigates the sentiment analysis of the” scholarship system” [4], in Odisha, primarily, to identify why some students do not apply for government-sponsored scholarships. Our research focuses on social media platforms, surveys, and machine learning-based analyses to understand the decision-making process and increase awareness about the various scholarship schemes. The goal of our experiment is to determine the efficacy of sentiment analysis in evaluating the effectiveness of different scholarship schemes. A wide variety of techniques based on dictionaries; corpora lexicons are used in different scholarship schemes for sentiment analysis. Our research paper is based on an evaluation process that could have a positive effect on the government by improving scholarship programs and giving financial aid to students from poor families, which would raise the level of education in Odisha. Our research paper concludes with a summary of successful and unsuccessful schemes, as well as their Word frequency counts and Sentiment Polarity scores.

Cybersecurity Risk and Audit Pricing—A Machine Learning-Based Analysis

ABSTRACT Cybersecurity risk represents a growing business threat. However, little attention has been paid to its assessment. This study proposes a machine learning algorithm that considers firm cybersecurity risk disclosure, information technology governance, external monitoring by financial analysts and auditors, and general firm characteristics to estimate cybersecurity risk (i.e., the likelihood of a firm experiencing data breaches during a year). This measure outperforms the measure produced by logistic regression models, is higher in industries more prone to cyberattacks, and effectively predicts future data breaches and firm use of cybersecurity insurance policies. I also examine whether auditors consider firm cybersecurity risk in the engagement planning process, finding that, on average, a one-percentage-point increase in cybersecurity risk is associated with a 1.15 percent increase in audit fees. In addition, auditors charge a fee premium after a data breach only if the client has heightened cybersecurity risk. Data Availability: Data are available from the public sources cited in the text.