Machine Learning Algorithms Research Articles

A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction

School principals encounter contemporary demands that impact their job satisfaction and leadership effectiveness. Despite the significance of this issue, there is limited research on satisfaction predictors for these professionals, particularly using machine learning approaches. This study identified key predictors of job satisfaction among Peruvian school principals by applying an ensemble of feature selection methods and evaluating five machine learning algorithms (Random Forest, Decision Trees-CART, Histogram-Based Gradient Boosting, XGBoost, and LightGBM) with data from the 2018 National Survey of Directors. The principal variables identified included satisfaction with salary, geographic location of the educational institution, relationships with students and teachers, workplace climate, student learning achievements, and job benefits. Economic factors proved important, such as gross and net income, and the minimum monthly amount required to meet household needs. Time-related aspects also exerted influence, including hours dedicated to training, time spent on administrative and/or teaching duties outside working hours, travel time to and from the Local Educational Management Unit (UGEL), duration of stays at the UGEL, and commuting time from principal residence to the educational institution. The Histogram-Based Gradient Boosting algorithm, optimized with Bayesian techniques and trained with data balanced through Random Oversampling, achieved a balanced accuracy of 0.63 on a test set with real-world class distribution. When using Generative Adversarial Networks to balance only the training set, better results were obtained in recall (0.74), precision (0.72), and F1 score (0.70). SHAP analysis revealed that economic factors primarily influenced dissatisfied principals, while interpersonal factors were more important for highly satisfied principals, suggesting a hierarchical pattern of needs. The findings could inform strategies to enhance principals' job satisfaction and strengthen data-driven educational policies.

Prediction of Mechanical Properties in Friction Stir Welded Al6061 Alloys Using Machine Learning Algorithms

Prediction of Mechanical Properties in Friction Stir Welded Al6061 Alloys Using Machine Learning Algorithms

Identification of genetic indicators linked to immunological infiltration in idiopathic pulmonary fibrosis.

This study employed bioinformatics to investigate potential molecular markers associated with idiopathic pulmonary fibrosis (IPF) and examined their correlation with immune-infiltrating cells. Microarray data for IPF were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and module genes were identified through Limma analysis and weighted gene co-expression network analysis. Enrichment analysis and protein-protein interaction network development were performed on the DEGs. Machine learning algorithms, including least absolute shrinkage and selection operator regression, random forest, and extreme gradient boosting, were applied to identify potential key genes. The predictive accuracy was assessed through a nomogram and a receiver operating characteristic (ROC) curve. Additionally, the correlation between core genes and immune-infiltrating cells was assessed utilizing the CIBERSORT algorithm. An IPF model was established in a human fetal lung fibroblast 1 (HFL-1) through induction with transforming growth factor β1 (TGF-β1), and validation was conducted via reverse transcription-quantitative polymerase chain reaction. A sum of 1246 genes exhibited upregulation, whereas 879 genes were downregulated. Pathway enrichment analysis and functional annotation revealed that DEGs were predominantly involved in extracellular processes. Four key genes - cd19, cxcl13, fcrl5, and slamf7 - were identified. Furthermore, ROC analysis demonstrated high predictive accuracy for these 4 genes. Compared to healthy individuals, lung tissues from IPF patients exhibited an increased presence of plasma cells, CD4 memory-activated T cells, M0 macrophages, activated dendritic cells, resting NK cells, and M2 macrophage infiltration. The upregulation of cd19, cxcl13, fcrl5, and slamf7 in TGF-β1-treated HFL-1 cells was confirmed, aligning with the findings from the microarray data analysis. cd19, cxcl13, fcrl5, and slamf7 serve as diagnostic markers for IPF, providing fresh perspectives regarding the fundamental pathogenesis and molecular mechanisms associated with this condition.

Migraine triggers, phases, and classification using machine learning models

BackgroundIn many countries, patients with headache disorders such as migraine remain under-recognized and under-diagnosed. Patients affected by these disorders are often unaware of the seriousness of their conditions, as headaches are neither fatal nor contagious. In many cases, patients with migraine are often misdiagnosed as regular headaches.MethodsIn this article, we present a study on migraine, covering known triggers, different phases, classification of migraine into different types based on clinical studies, and the use of various machine learning algorithms such as logistic regression (LR), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) to learn and classify different migraine types. This study will only consider using these methods for diagnostic purposes. Models based on these algorithms are then trained using the dataset, which includes a compilation of the types of migraine experienced by various patients. These models are then used to classify the types of migraines, and the results are analyzed.ResultsThe results of the machine learning models trained on the dataset are verified for their performance. The results are further evaluated by selective sampling and tuning, and improved performance is observed. The precision and accuracy obtained by the support vector machine and artificial neural network are 91% compared to logistic regression (90%) and random forest (87%). These models are run with the dataset without optimal tuning across the entire dataset for different migraine types; which is further improved with selective sampling and optimal tuning. These results indicate that the discussed models are relatively good and can be used with high precision and accuracy for diagnosing different types of migraine.ConclusionOur study presents a realistic assessment of promising models that are dependable in aiding physicians. The study shows the performance of various models based on the classification metrics computed for each model. It is evident from the results that the artificial neural network (ANN) performs better, irrespective of the sampling techniques used. With these machine learning models, types of migraines can be classified with high accuracy and reliability, enabling physicians to make timely clinical diagnoses of patients.

Smart Posture Correction Wearable for Individuals with Cerebral Palsy

Abstract – The smart Posture Correction Wearable is designed to assist individuals with cerebral palsy (CP) in maintaining proper posture through real-time feedback. The device utilizes posture sensors and a haptic feedback mechanism to detect misalignments and provide gentle vibrations or alerts for correction. The methodology involved designing a wearable system with embedded sensors, processing real-time data using machine learning algorithms, and integrating the device with a mobile application for posture tracking. A pilot study was conducted on individuals with CP over four weeks, where data on posture deviations, user responses, and comfort levels were recorded. The mobile app provided data-driven insights, enabling users to track progress and adjust their posture accordingly. The lightweight and adjustable design ensured comfort, making it suitable for prolonged use. Overall, the wearable technology demonstrated significant potential in enhancing postural alignment, reducing the risk of musculoskeletal issues, and improving the quality of life for individuals with CP. Future enhancements will focus on increasing sensor precision and expanding clinical trials for broader validation. Keywords: Smart Posture Correction, Wearable Device, Cerebral Palsy, Rehabilitation Technology, Quality of Life.

Can customers be hygiene inspectors? Leveraging social media to support health authorities in food safety monitoring

PurposeSome restaurant customers who contract foodborne illnesses do not contact public health authorities but instead post online reviews to social media. By monitoring social media discourse, health authorities can gather information updates about restaurants’ hygiene deficiencies and thereby identify potential venues for outbreaks of foodborne illness. This study proposes a social media analytics framework to analyze the associations among negative hygiene aspects mentioned in customers’ reviews and use those associations to predict restaurants’ food safety.Design/methodology/approachThis study analyzes customer reviews of restaurants and identifies the co-occurrence patterns of hygiene-related keywords. To assess the extent to which the word co-occurrences are effective in preventing foodborne illnesses, classification models were constructed to use those co-occurrences as inputs to predict restaurants’ food safety risk.FindingsThis study obtains 20 association rules that reveal the co-occurrences of hygiene-related keywords. Using those co-occurrences as inputs, our best-performing model can detect 87.58% of high-risk restaurants.Practical implicationsWhen monitoring social media, health authorities can focus on a group of keywords and deploy our model to identify restaurants that are likely to contribute to foodborne illnesses.Originality/valueThrough the lens of signaling theory, this study is a pioneering work to reduce the dimensionality of social media data to a few meaningful hygiene-related keywords, filtering out irrelevant signals that disturb the signaling process. Social media data, after being processed by appropriate machine learning algorithms, become credible signals for risk prediction.

Combined assessment of stress hyperglycemia ratio and glycemic variability to predict all-cause mortality in critically ill patients with atherosclerotic cardiovascular diseases across different glucose metabolic states: an observational cohort study with machine learning

BackgroundStress hyperglycemia ratio (SHR) and glycemic variability (GV) reflect acute glucose elevation and fluctuations, which correlate with adverse outcomes in patients with atherosclerotic cardiovascular disease (ASCVD). However, the prognostic significance of combined SHR-GV evaluation for ASCVD mortality remains unclear. This study examines associations of SHR, GV, and their synergistic effects with mortality in patients with ASCVD across different glucose metabolic states, incorporating machine learning (ML) to identify critical risk factors influencing mortality.MethodsPatients with ASCVD were screened in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and stratified into normal glucose regulation (NGR), pre-diabetes mellitus (Pre-DM), and diabetes mellitus (DM) groups based on glucose metabolic status. The primary endpoint was 28-day mortality, with 90-day mortality as the secondary outcome. SHR and GV levels were categorized into tertiles. Associations with mortality were analyzed using Kaplan-Meier(KM) curves, Cox proportional hazards models, restricted cubic splines (RCS), receiver operating characteristic (ROC) curves, landmark analyses, and subgroup analyses. Five ML algorithms were employed for mortality risk prediction, with SHapley Additive exPlanations (SHAP) applied to identify critical predictors.ResultsA total of 2807 patients were included, with a median age of 71 years, and 58.78% were male. Overall, 483 (23.14%) and 608 (29.13%) patients died within 28 and 90 days of ICU admission, respectively. In NGR and Pre-DM subgroups, combined SHR-GV assessment demonstrated superior predictive performance for 28-day mortality versus SHR alone [NGR: AUC 0.688 (0.636–0.739) vs. 0.623 (0.568–0.679), P = 0.028; Pre-DM: 0.712 (0.659–0.764) vs. 0.639 (0.582–0.696), P = 0.102] and GV alone [NGR: 0.688 vs. 0.578 (0.524–0.633), P < 0.001; Pre-DM: 0.712 vs. 0.593 (0.524–0.652), P < 0.001]. Consistent findings were observed for 90-day mortality prediction. However, in the DM subgroup, combined assessment improved prediction only for 90-day mortality vs. SHR alone [AUC 0.578 (0.541–0.616) vs. 0.560 (0.520–0.599), P = 0.027], without significant advantages in other comparisons.ConclusionsCombined SHR and GV assessment serves as a critical prognostic tool for ASCVD mortality, providing enhanced predictive accuracy compared to individual metrics, particularly in NGR and Pre-DM patients. This integrated approach could inform personalized glycemic management strategies, potentially improving clinical outcomes.Graphic abstract

Development and Validation of an Explainable Machine Learning Model for Warning of Hepatitis E Virus-Related Acute Liver Failure.

Early identification of patients with acute hepatitis E (AHE) who are at high risk of progressing to hepatitis E virus-related acute liver failure (HEV-ALF) is crucial for enabling timely monitoring and intervention. This multicentre retrospective cohort study aimed to develop and validate an interpretable machine learning (ML) model for predicting the risk of HEV-ALF in hospitalised patients with AHE in tertiary care settings. The study cohort included patients admitted to seven tertiary medical centers in Jiangsu, China, between 01 January 2018 and 31 December 2024. Multiple ML algorithms were applied for feature selection and model training. The predictive performance of the models was evaluated in terms of discrimination, calibration and clinical net benefit. The interpretability of the final model was enhanced using the SHapley Additive exPlanations. A total of 1912 participants were included in the study. Ten ML models were developed based on seven consensus-selected baseline features, with the survival gradient boosting machine (GBM) demonstrating superior performance compared to the traditional Cox proportional hazards regression model and other relevant models or scores. The GBM model achieved a Harrell's concordance index of 0.853 (95% CI: 0.791-0.914) in the external validation set. To facilitate clinical application, the GBM model was interpreted globally and locally and deployed as a web-based tool using the Streamlit-Python framework. The GBM model demonstrated excellent performance in predicting HEV-ALF risk in hospitalised patients with AHE, offering a promising tool for clinical decision-making.

ROLE OF ARTIFICIAL INTELLIGENCE AND AUTOMATIONINCLOUD ACCOUNTING IN SELECTED LOGISTICS COMPANIESINCHENNAI DISTRICT

Management in the logistics industry has undergone a substantial transformation thanks totheintegration of automation and artificial intelligence (AI) in cloud accounting. This studyexamines how AI-driven cloud accounting solutions affect operational effectiveness, financial accuracy, and decision-making in a subset of logistics firms in the Chennai area. The study looks at how AI-powered solutions, like predictive analytics, machine learningalgorithms, and robotic process automation (RPA), simplify accounting duties like financial reporting, fraud detection, tax compliance, and invoice processing. Furthermore, real-timeaccess to financial data is provided by cloud-based technologies, which enhance teamworkand lower human error.

Helperly: An All-Inclusive Healthcare Application

This work presents the development of a comprehensive healthcare app designed to improve early disease detection and enhance healthcare accessibility. The application integrates cutting-edge yet lightweight machine learning (ML) algorithms like Multinomial Naive Bayes and Decision Tree for symptom analysis and incorporates a range of innovative healthcare APIs like Edamam and Exercise API by Ninjas. Its primary objectives include empowering users with proactive health insights, facilitating timely medical assistance, and promoting overall well-being through personalised health recommendations. Key features of the app include accurate disease prediction through ML-driven symptom analysis, healthy recipe recommendations, customised exercise plans, and a conversational chatbot for diagnosis and treatment suggestions. By leveraging these functionalities, the app aims to enable users to take control of their health effectively, promoting paperless transactions via digital appointment and prescriptions. It also reduces physical visits to healthcare facilities, lowering carbon emissions associated with travel, which eventually paves the way to reduce environmental impact. The database integration via Firebase Auth offers data accessibility and security to data via services like encryption and Cloud Store. The intuitive navigation through the chatbot makes it approachable for users, including those who are less tech-savvy. Dark mode support aligns with sustainability goals by reducing eye strain and energy consumption. Thus, the work adheres to material design principles. With a user-centric approach, this app combines innovative ML-driven features and healthcare APIs to set a new standard in the digital health space, paving the way for advancements in early detection, personalised care, accessible healthcare services and long-term societal impact.

Machine and deep learning for personality traits detection: a comprehensive survey and open research challenges

Natural language processing (NLP), a prominent research domain of Artificial Intelligence (AI), analyzes users’ generated content on social media for various purposes like sentiment analysis, text summarization, chatbots, fake news detection, etc. Recent advancements in NLP have helped for analysis of human behavior analysis and predicting various human personality traits. Understanding personality traits has long been a fundamental pursuit in psychology and cognitive sciences due to its vast applications for understanding from individuals to social dynamics. Due to online social platforms where people express their views, experiences and comments, NLP is applied for users’ behavior and personality analysis, which is helpful in defining marketing strategies, consumers’ behavior analysis, team building, etc. This research study provides a comprehensive overview of existing methodologies, applications, and challenges in the field of personality traits detection using shallow machine learning, ensemble learning and deep learning. To conduct this study, recent research publications relevant to NLP for this new but emerging research domain are reviewed. The background knowledge of personality models of various nature is discussed for better domain understanding. The study encompasses machine learning and deep learning models with thorough analysis of traditional and innovative techniques including ensemble learning and transformer-based models in chronological order highlighting the trend analysis showing evolution of application of advanced methods. The review also presents and compares the widely used datasets which may guide the researchers for selection of datasets in future studies. Performance evaluation metrics have been discussed which are used in the relevant literature. Furthermore, it explores the application of research of personality traits detection in various domains highlighting its significance. We have also carried out extensive empirical analysis using conventional textual to advanced deep embedding features and applying machine learning, ensemble learning and deep learning algorithms. Finally, before conclusion, the review highlights the open research issues and challenges as potential future directions for the researchers.

Artificial Intelligence Techniques for Landslides Prediction Using Satellite Imagery

Landslides in hilly areas can be triggered by natural factors like heavy rainfall and earthquakes, or by human activities such as unplanned construction. These events often result in significant loss of life and property. Machine learning (ML) and deep learning (DL) algorithms have been increasingly used for automatic landslide detection from satellite images. While there has been progress in semiautomatic detection, fully automatic systems with high accuracy are still limited. One of the biggest challenges is the lack of appropriate training datasets. This study reviews various ML and DL techniques for landslide classification and identifies research gaps. It proposes a novel prototype using a modified ResNet101 deep learning model, achieving an accuracy of 96.88% on an augmented Beijing satellite dataset. The findings offer valuable insights for future research in landslide detection and classification using satellite imagery

Machine learning predictions of drug release from isocyanate-derived aerogels.

This work utilized machine learning (ML) algorithms to predict and validate the in vitro drug release kinetics of a short worm-like nanostructured isocyanate-derived aerogel: the first time ML has been employed to study the drug delivery properties of this important class of materials. The algorithms were first trained with sixteen datasets, each containing eight release data points, before using them to predict the release profiles of the unknown. The predicted data was validated via the random sampling and cross-validation techniques. In both instances, the established models were used to predict the release kinetics of four aerogel nanostructures with known experimental release profiles. A good correlation between the experimental and predicted release profiles was observed, with gradient boosting being the best-performing algorithm (R2 > 0.9). Furthermore, the ranking of the importance of each input feature for drug release from the aerogels aligns with previous studies, validating the rationale behind the modeling. Morphology, quantified by the K-index (contact angle/porosity), and the macropore-to-mesopore ratios were found to be the most influential factors, after time, in determining drug release profiles. The findings from this study suggest that ML can serve as a valuable tool for predicting the drug release kinetics of aerogels, thereby saving time and cost involved in conducting laborious drug delivery experiments. We envisage that this study will provide a foundation for future related computational works and reduce the trial-and-error experimental approach to solving scientific problems.

Prediction of Chemical Reactivity Parameters via Data‐Driven Approach

AbstractNovel material designing in an efficient way and its property prediction is empowered by data‐driven approach. For system designing or synthesis, stable and compatible chemical counterparts containing functional materials are preferred. In this regard, the knowledge of chemical reactivity is indispensable and is closely associated with how a substance reacts in a particular chemical reaction. In this work, chemical reactivity parameters of some organic molecules through machine learning (ML) algorithms are predicted. Several categories of descriptors are used as input features to predict HOMO‐LUMO energy gap, ionization potential, electron affinity, chemical potential, chemical hardness and electrophilicity index. The accurately achieved reactivity parameters confirm the descent training of the model from the integrated data of organic molecules. This work confirms that chemical properties reproduced through ML approach are closely correlated with density functional theory (DFT) ‐based results, so the proposed ML approach provides reactivity information at a very cheap cost. The prediction of chemical reactivity, as well as perception of the correlations between input features and targeted properties of organic molecules, may lead the experimentalist to know more about the observation.

Review of artificial intelligence applications in geothermal energy extraction from hot dry rock

AbstractThe geothermal resources in hot dry rock (HDR) are considered the future trend in geothermal energy extraction due to their abundant reserves. However, exploitation of the resources is fraught with complexity and technical challenges arising from their unique characteristics of high temperature, high strength, and low permeability. With the continuous advancement of artificial intelligence (AI) technology, intelligent algorithms such as machine learning and evolutionary algorithms are gradually replacing or assisting traditional research methods, providing new solutions for HDR geothermal resource exploitation. This study first provides an overview of HDR geothermal resource exploitation technologies and AI methods. Then, the latest research progress is systematically reviewed in AI applications in HDR geothermal reservoir characterization, deep drilling, heat production, and operational parameter optimization. Additionally, this study discusses the potential limitations of AI methods in HDR geothermal resource exploitation and highlights the corresponding opportunities for AI's application. Notably, the study proposes the framework of an intelligent HDR exploitation system, offering a valuable reference for future research and practice.

AI-Driven Advances in Parkinson’s Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor dysfunctions that severely compromise patients’ quality of life. While pharmacological treatments provide symptomatic relief in the early stages, advanced PD often requires neurosurgical interventions, such as deep brain stimulation (DBS) and focused ultrasound (FUS), for effective symptom management. A significant challenge in optimizing these therapeutic strategies is the early identification and recruitment of suitable candidates for clinical trials. This review explores the role of artificial intelligence (AI) in advancing neurosurgical and neuroscience interventions for PD, highlighting the ways in which AI-driven platforms are transforming clinical trial design and patient selection. Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. These innovations improve trial efficiency, broaden treatment options, and enhance patient outcomes. However, integrating AI into clinical trial frameworks presents challenges such as data standardization, regulatory hurdles, and the need for extensive validation. Addressing these obstacles will require collaboration among neurosurgeons, neuroscientists, AI specialists, and regulatory bodies to establish ethical and effective guidelines for AI-driven technologies in PD neurosurgical research. This paper emphasizes the transformative potential of AI and technological innovation in shaping the future of PD neurosurgery, ultimately enhancing therapeutic efficacy and patient care.

Twitter Bot Detection Using Machine Learning and Deep Learning Techniques

Abstract—The proliferation of Twitter bots poses a serious threat to the reliability of online conversations and results in disinformation, spam, and opinion manipulation. This paper presents a comprehensive examination of Twitter bot detection techniques with traditional machine learning (ML) algorithms contrasted with cutting-edge deep learning (DL) models. Key fea- tures like tweet frequency, follower-following ratios, user behavior patterns, and content features are investigated. We compare algorithms like Random Forest, Support Vector Machines (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Long Short- Term Memory (LSTM), and Recurrent Neural Networks (RNN) based on accuracy, precision, recall, and F1-score metrics. Our experiments showed that Random Forest was the best with the highest accuracy and thus, it is the best-suited model for the dataset used in this experiment. We also address the issues of real- time bot detection, the limitation of single models, and suggest a hybrid approach that takes advantage of the strengths of both ML and DL approaches for better performance. Index Terms—Twitter Bot Detection, Machine Learning, Deep Learning, Social Network Analysis, Random Forest, Support Vec- tor Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN).

AI-Powered Talent Acquisition Platform

Abstract -The fast-paced changing job market creates serious challenges in talent attraction, requiring more effective and data-centric methods to automate the recruitment process. This paper introduces JobPathway, a full-fledged MERN stack application to maximize talent attraction using AI-driven resume parsing and job and profile reminder personalization. Utilizing sophisticated machine learning algorithms, JobPathway processes candidate profiles, identifies them with suitable job openings, and provides timely alerts in order to increase interactions and avoid lost opportunities. Further, the platform is envisioned to have quiz and interview planning functionalities so that recruiters and candidates can conduct the entire recruitment process in one integrated interface. This paper describes the system architecture, main components, and possible effect of JobPathway in improving recruitment efficiency and user experience. Key Words: Talent Acquisition, Artificial Intelligence (AI), Machine Learning (ML), Resume Parsing, Job Matching, Skill Recommendations, Predictive Analytics, Recruitment Systems, Role-Based Access Control, Job Seekers, Recruiters, Candidate Profiling, Automated Hiring, AI-powered Recruiting, Job Recommendation Systems, Behavioral Assessment, Interview Scheduling, Predictive Reminders, AI in HR, Recruitment Automation, AI-driven Talent Management, Node.js, Express, MongoDB, JWT, OAuth, Axios, Bcrypt, Frontend Development, MERN Stack, Backend Development, Web Development, Cloud Computing, REST APIs, Authentication, Authorization, Cloudinary, Responsive Design, Web Security.

Deep Learning Based Phishing Detection System

Malicious URLs and websites continue to undermine online security, with search engines inadvertently becoming platforms for fraudulent sites. Traditional phishing detection methods often based on white lists, blacklists, or single-model approaches fail to address the evolving sophistication of phishing attacks. This persistent threat highlights the urgent need for more advanced and adaptive security measures that can reliably identify unsafe URLs in real time. Motivated by these challenges, our research introduces an enhanced phishing detection framework that integrates Natural Language Processing (NLP) with a combination of machine learning algorithms specifically, Support Vector Machine (SVM), Random Forest, and Decision Tree. The SVM algorithm is chosen for its robustness in handling high-dimensional data, while Random Forest and Decision Tree contribute through ensemble learning and interpretability, respectively. Together, these methods form a comprehensive system that accurately differentiates between malicious and legitimate URLs. Additionally, the system incorporates AES encryption to secure sensitive user data, ensuring that browsing history and other critical information remain confidential. The significance of this research lies in its dual contribution to improving cybersecurity and safeguarding user privacy. By effectively detecting phishing attempts and encrypting user data, our approach not only mitigates the risks associated with malicious URLs but also establishes a higher standard for protecting sensitive information online. This integrated solution paves the way for more resilient defences against phishing attacks, offering users enhanced security without compromising privacy.

Early Prediction of Septic Shock in Emergency Department Using Serum Metabolites.

Early recognition of septic shock is crucial for improving clinical management and patient outcomes, especially in the emergency department (ED). This study conducted serum metabolomic profiling on ED patients diagnosed with septic shock (n = 32) and those without septic shock (n = 92) using a high-resolution mass spectrometer. By implementing a supervised machine learning algorithm, a prediction model based on a panel of metabolites achieved an accuracy of 87.8%. Notably, when employed on a low-resolution instrument, the model maintained its predictive performance with an accuracy of 84.2%. These results demonstrate the potential of metabolite-based algorithms to identify patients at high risk of septic shock. Our proposed workflow aims to optimize risk assessment and streamline clinical management processes in the ED, holding promise as an efficient routine test to promote timely intensive interventions and reduce septic shock mortality.