Network Traffic Classification Using Machine Learning, Transformer, and Large Language Models

Integrated STEM projects and experiments promote interdisciplinary learning, enhance critical thinking, and prepare students for real-world problem-solving by bridging science, technology, engineering, and mathematics. While numerous research studies have explored the STEM education domain, few have focused on supporting educators in identifying and designing effective integrated STEM projects and experiments. This research addresses this gap by introducing a novel system that leverages machine learning (ML) and large language models (LLMs) to assist educators in classifying and creating integrated STEM projects.We created and labeled a dataset of 1,071 project descriptions in this research work, mapped to one or more STEM areas (Science, Technology, Engineering, Mathematics). The dataset was developed using manually curated records, harvesting online sources, and synthetic data generation. Natural Language Processing (NLP) techniques were applied to prepare project descriptions for analysis. Various ML models, including Random Forest, Support Vector Machines (SVM), Logistic Regression, and Naive Bayes, were used for multi-label classification. The Random Forest model achieved the best performance, with a Precision of 91.99%, Recall of 94.59%, F1 Score of 93.27%, and Hamming Loss of 6.74%.The system operates in two phases: In Phase 1, the instructor proposes a project title with a description, and in response, the ML model identifies one or more STEM areas as the most relevant. In Phase 2, ChatGPT, specifically GPT-4o, assists educators by defining experiment objectives, generating ideas, designing procedures, and suggesting tools and resources. This human-in-the-loop system enhances collaboration between educators, ML models, and LLMs, streamlining the design of effective integrated STEM projects.This work's implications extend to improving the accessibility and effectiveness of integrated STEM education by reducing the barriers educators face in designing interdisciplinary projects. It provides a scalable framework for integrating advanced AI techniques into educational practices and fostering classroom innovation.

Global health is currently facing serious challenges due to the increasing number of chronic disease patients, such as those with heart failure, diabetes, and cancer. This issue arises from the limitations of electronic health record (EHR) systems, which are not yet fully capable of ensuring accurate clinical diagnoses because of potential data input errors and delays in symptom identification by medical personnel. In response to this issue, this paper focuses on the integration of medical tabular data with a classification approach based on classical machine learning (ML) and large language models (LLM) to improve the accuracy of patient diagnosis predictions. This paper aims to develop and compare the performance of various ML models, such as XGBoost, SVM, and logistic regression, as well as LLM models like Gemini, LLaMA, and Qwen in fine-tuning, few-shot, and zero-shot scenarios. The paper results show that the combination of Gemini and the few-shot approach (250 shots) achieved the highest accuracy of up to 99.8% in predicting heart failure risk. The main finding of this study is that the narrative text representation of tabular data processed with LLM significantly enhances contextual understanding and classification accuracy, making this approach highly potent for application in AI-based clinical decision-making.

SummarySustained engagement in HIV care and adherence to antiretroviral therapy (ART) are essential for achieving the UNAIDS “95-95-95” targets. Despite increased ART access, disengagement from care remains a significant issue, particularly in sub-Saharan Africa. Traditional machine learning (ML) models have shown moderate success in predicting care disengagement, which would enable early intervention. We develop an enhanced large language model (LLM) fine-tuned with electronic medical records (EMRs) to predict people at risk of disengaging from HIV care in Tanzania and to provide interpretative insights into modifiable risk factors.MethodsWe developed a novel AI model by enhancing a pre-trained LLM (LLaMA 3.1, an open-source pre-trained LLM released by Meta) using routinely collected EMRs from Tanzania’s National HIV Care and Treatment Program from January 1, 2018, to June 30, 2023 (4,809,765 records for 261,192 people) to identify people at risk of disengaging from HIV care or developing adverse outcomes. Outcomes included risk of ART non-adherence, non-suppressed viral load, and loss to follow-up. Models were evaluated internally (Kagera region) and externally (Geita region), with performance compared against state-of-art ML models and zero-shot LLMs. Additionally, a team of HIV physicians in Tanzania assessed the LLM’s predictions along with LLM provided justifications for a subset of patient records to evaluate their clinical relevance and reasoning.FindingsThe enhanced LLMs consistently outperformed the supervised ML model and zero-shot LLMs across all outcomes in both internal and external validation datasets. When focusing on the 25% of PLHIV predicted as most likely to lost-to-follow-up (LTFU), the model correctly identified 78% (2,515 of 3,224) of people living with HIV (PLHIV) genuinely at risk in internal validation and 73% (7,105 of 9,733) in external validation. Attention score analysis indicated that the enhanced LLM focused on keywords such as gaps in follow-up care and ART adherence. The human expert evaluation showed alignment between clinician assessments and the LLM’s predictions in 65% of cases, with experts finding the model’s justifications reasonable and clinically relevant in 92.3% of aligned cases.InterpretationIf implemented in HIV clinics, this LLM-based AI model could help allocate resources efficiently and deliver targeted interventions, improving retention in care and advancing the UNAIDS “95-95-95” targets. By functioning like a clinician–analyzing patient summaries, predicting risks, and offering reasoning–the enhanced LLM could be integrated into clinical workflows to complement human expertise, facilitating timely interventions and informed decision-making. If implemented widely, this human-AI collaboration has the potential to improve health outcomes for people living with HIV and reduce onward transmission.FundingThe study was supported by a grant from the US National Institutes of Health (NIH): NIH NIMH 1R01MH125746.

Software vulnerabilities pose significant risks to the security and reliability of modern systems, making automated vulnerability detection an essential research area. Traditional static and rule-based approaches are limited in scalability and adaptability, motivating the adoption of data-driven methods. In this survey, we present a comprehensive review of Machine Learning (ML), Deep Learning (DL), and Large Language Models (LLMs) techniques for vulnerability detection. We analyze recent advances in feature representation, fine-tuning strategies, generative approaches, and prompt engineering, while highlighting their ability to capture both syntactic and semantic properties of source code. Furthermore, we examine commonly used evaluation metrics and provide a critical discussion of key challenges, including the lack of large-scale real-world datasets, limited vulnerability coverage, class imbalance, interpretability gaps, hallucination, and high computational costs. To address these issues, we outline promising future research directions, such as neuro-symbolic hybrid methods, parameter-efficient fine-tuning, continual learning, cross-language generalization, and explainable AI for vulnerability detection. Unlike previous studies, the present work explores learning paradigms from ML to LLMs using comprehensive evaluation criteria that highlight analytical capability, feature interpretability, and code-context comprehension. By combining these factors, our study addresses the methodological gap between classic feature-based approaches and current LLM-driven reasoning frameworks, providing beneficial insights to develop robust, scalable, and trustworthy software vulnerability detection systems.

Occupational stress is a major concern for employers and organizations as it compromises decision-making and overall safety of workers. Studies indicate that work-stress contributes to severe mental strain, increased accident rates, and in extreme cases, even suicides. This study aims to enhance early detection of occupational stress through machine learning (ML) methods, providing stakeholders with better insights into the underlying causes of stress to improve occupational safety. Utilizing a newly published workplace survey dataset, we developed a novel feature selection pipeline identifying 39 key indicators of work-stress. An ensemble of three ML models achieved a state-of-the-art accuracy of 90.32%, surpassing existing studies. The framework’s generalizability was confirmed through a three-step validation technique: holdout-validation, 10-fold cross-validation, and external-validation with synthetic data generation, achieving an accuracy of 89% on unseen data. We also introduced a 1D-CNN to enable hierarchical and temporal learning from the data. Additionally, we created an algorithm to convert tabular data into texts with 100% information retention, facilitating domain analysis with large language models, revealing that occupational stress is more closely related to the biomedical domain than clinical or generalist domains. Ablation studies reinforced our feature selection pipeline, and revealed sociodemographic features as the most important. Explainable AI techniques identified excessive workload and ambiguity (27%), poor communication (17%), and a positive work environment (16%) as key stress factors. Unlike previous studies relying on clinical settings or biomarkers, our approach streamlines stress detection from simple survey questions, offering a real-time, deployable tool for periodic stress assessment in workplaces.

Artificial intelligence (AI) technologies, including machine learning (ML), deep learning, and large language models, are increasingly applied in medical diagnostics. In rhinology, these tools are being evaluated for tasks such as image interpretation, cytology classification, and clinical decision support. To systematically evaluate the application and diagnostic accuracy of AI technologies in rhinology, with a focus on clinical utility and implementation barriers. This review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Seventeen full-text studies were screened based on predefined eligibility criteria, focusing on AI applications with diagnostic metrics in rhinology. Data on AI type, diagnostic task, performance outcomes, and study quality were extracted and synthesized narratively. Twelve studies met the inclusion criteria. Image-based diagnostic tools using convolutional neural networks demonstrated high accuracy (81%-99%) in nasal polyp detection, cytology classification, and computed tomography (CT) scan interpretation. ML models using patient-reported data achieved accuracies of 74.5%-85.5% for chronic rhinosinusitis prediction. Large language models like ChatGPT and Gemini were evaluated for clinical question answering, with performance exceeding 80% in some domains. Risk of bias was moderate in most primary studies, and none reported clinical integration beyond prototype stages. AI exhibits promising diagnostic accuracy across several applications in rhinology. However, significant challenges persist, including limited validation, methodological heterogeneity, and lack of clinical implementation. Future research should focus on prospective trials, explainability, and regulatory frameworks to ensure safe integration into clinical workflows.

Sentiment analysis, in the context of digital banking reviews, aims to assess customer satisfaction and support service enhancement. Despite increasing attention to sentiment analysis across domains, Arabic banking reviews remain underexplored. To bridge this gap, we introduce a dataset of 4922 Arabic reviews from three major Saudi digital banks with three sentiment categories positive, negative, or conflict—providing actionable insights for banks. We evaluate the dataset using several machine learning models and four large language models (LLMs)—GPT 3.5, GPT 4, Llama-3-8B-Instruct, and SILMA—using zero-shot (no labeled examples) and few-shot (a few labeled examples) learning strategies. Our results show that GPT 4 performs best among LLMs in few-shot settings, while traditional models still outperform LLMs, with a Voting Classifier achieving 90.24% accuracy. This study contributes a domain-specific dataset and comparative analysis to support research and practical improvements in Arabic digital banking services.

Food is an essential component in living beings and for that we need agriculture and soil type detection is an essential part of agriculture. To help with that we have created a model where the model gives a hybrid decision support system accurately modeled to reduce the increasing risk of crop failure caused by unstable climatic conditions and soil nutrient differences. When the global agricultural environment drops the dependency in traditional farming, this study shows a strong computational methodology that combines mainly three particular layers to provide highly precise guidance. The starting layer in our model uses Random Forest machine learning model that focuses on soil classification based on soil type. The model then checks essential soil parameters that include Nitrogen (N), Phosphorus (P), and Potassium (K) levels along with the pH. The second layer brings the use of a Geospatial API needed for a 10 year longitudinal climate check in the second layer. The data of historical temperature, rainfall with local soil chemistry helps to calculate the environmental problems. The third layer is the Mistral 7B Large Language Model LLM, which is used in practical farming advice. Machine learning handles quantitative data while the LLM gives a practical context so that it offers agricultural related advice to support data results with real farming experience. The output lists the top five probable crops according to climate changes and difficulty risk scores. This dual focus on soil health and climate gives assurance on productive advice and continuity under volatile situations. Validation results indicate that the model performs almost flawlessly with benchmark data. By connecting the fixed set of probabilistic distribution of the Random Forest with the adaptive intelligence of a Large Language Model, the model provides a comprehensive decision-support tool with fewer limitations from the single-model machine learning applications.

We present a novel multi-agent framework that integrates interpretable machine learning (ML) models with large language models (LLMs) to deliver personalized credit risk recommendations. Using the Lending Club Loan dataset, we train an XGBoost based credit risk model, applying SHAP (SHapley Additive exPlanations), we extract the top risk factors for individual borrowers. Subsequently, these personalized explanations are provided to an LLM, which generates actionable recommendations to help customers improve their credit profiles. A structured multi-agent LLM review pipeline is introduced to assess each recommendation for tone, compliance, and legal soundness. Recommendations scoring above a set threshold in all review dimensions are only shared with customers, ensuring fairness, transparency, and regulatory adherence. The experimental results demonstrate the effectiveness of the framework in providing clear, actionable, and compliant recommendations.

Artificial intelligence (AI) has the potential to enhance clinical decision-making, including in infectious diseases. By improving antimicrobial resistance prediction and optimizing antibiotic prescriptions, these technologies may support treatment strategies and address critical gaps in healthcare. This study evaluates the effectiveness of AI in guiding appropriate antibiotic prescriptions for infectious diseases through a systematic literature review. We conducted a systematic review of studies evaluating AI (machine learning or large language models) used for guidance on prescribing appropriate antibiotics in infectious disease cases. Searches were performed in PubMed, CINAHL, Embase, Scopus, Web of Science, and Google Scholar for articles published up to October 25, 2024. Inclusion criteria focused on studies assessing the performance of AI in clinical practice, with outcomes related to antimicrobial management and decision-making. Seventeen studies used machine learning as part of clinical decision support systems (CDSS). They improved prediction of antimicrobial resistance and optimized antimicrobial use. Six studies focused on large language models to guide antimicrobial therapy; they had higher prescribing error rates, patient safety risks, and needed precise prompts to ensure accurate responses. AI, particularly machine learning integrated into CDSS, holds promise in enhancing clinical decision-making and improving antimicrobial management. However, large language models currently lack the reliability required for complex clinical applications. The indispensable role of infectious disease specialists remains critical for ensuring accurate, personalized, and safe treatment strategies. Rigorous validation and regular updates are essential before the successful integration of AI into clinical practice.

BACKGROUND Accurately assessing perioperative risk is critical for informed surgical planning and patient safety. However, current prediction models often rely on structured data and overlook the nuanced clinical reasoning embedded in free-text preoperative notes. Recent advances in large language models (LLMs) have opened opportunities for harnessing unstructured clinical data, yet their application in perioperative prediction remains limited by concerns about factual accuracy. Retrieval-augmented generation (RAG) offers a promising solution—enhancing LLM performance by grounding outputs in domain-specific knowledge sources, potentially improving both predictive accuracy and clinical interpretability. OBJECTIVE This study aimed to investigate whether integrating LLMs with RAG can improve the prediction of 30-day postoperative mortality and American Society of Anesthesiologists (ASA) physical status classification using unstructured preoperative clinical notes. METHODS We conducted a retrospective cohort study using 24,491 medical records from a tertiary medical center, including preoperative anesthesia assessments, discharge summaries, and surgical information. To extract clinical insights from free-text data, we used the LLaMA 3.1-8B language model with RAG, using MedEmbed for text embedding and Miller’s Anesthesia as the primary retrieval source. We evaluated model performance under various configurations, including embedding models, chunk sizes, and few-shot prompting. Machine learning (ML) models, including random forest, support vector machines (SVM), Extreme Gradient Boosting (XGBoost), and logistic regression, were trained on structured features as baselines. RESULTS A total of 520 (2.1%) patients experienced in-hospital 30-day postoperative mortality. The ASA physical status distribution was as follows: class I: 535 (2.2%); class II: 15,272 (62.4%); class III: 8024 (32.8%); class IV: 606 (2.5%); and class V: 54 (0.22%). For 30-day postoperative mortality prediction, the LLaMA‑RAG model achieved an <i>F</i><sub>1</sub>-score of 0.4663 (95% CI 0.4654-0.4672), versus 0.2369 (95% CI 0.2341-0.2397) without few‑shot prompting, 0.0879 (95% CI 0.0717-0.1041) without RAG, and 0.0436 (95% CI 0.0292-0.0580) without either few‑shot prompting or RAG. Among ML models, XGBoost scored 0.4459 (95% CI 0.4176-0.4742); random forest, 0.3953 (95% CI 0.3791-0.4115); logistic regression, 0.2720 (95% CI 0.2647-0.2793); and SVM, 0.2474 (95% CI 0.2275-0.2673). For ASA classification, LLaMA‑RAG achieved a micro <i>F</i><sub>1</sub>-score of 0.8409 (95% CI 0.8238-0.8551) versus 0.6546 (95% CI 0.6430-0.6796) without few-shot prompting, 0.6340 (95% CI 0.6157-0.6535) without RAG, and 0.4238 (95% CI 0.3952-0.4490) without either few‑shot prompting or RAG. In comparison, XGBoost achieved 0.8273 (95% CI 0.8209-0.8498); logistic regression, 0.7940 (95% CI 0.7671-0.7950); random forest, 0.7847 (95% CI 0.7637-0.7868); and SVM, 0.7697 (95% CI 0.7637-0.7697). Notably, the model demonstrated exceptional sensitivity in identifying rare but high-risk cases, such as ASA Class 5 patients and postoperative deaths. CONCLUSIONS The LLaMA-RAG model significantly improved the prediction of postoperative mortality and ASA classification, especially for rare high-risk cases. By grounding outputs in domain knowledge, retrieval-augmented generation enhanced both accuracy and prompt‑driven interpretability over ML and ablation models—highlighting its promise for real-world clinical decision support. CLINICALTRIAL

Understanding the structure-activity relationship (SAR) of antibiotic scaffolds is crucial for the development of antibiotics to counter the growing crisis of antimicrobial resistant bacteria. However, an overwhelming space of structural features impairs a comprehensive understanding of the mechanism of action for potential antibiotic candidates. In this study, antibacterial data of a set of newly synthesized cyanine molecules are analyzed with both traditional machine learning (ML) and commercially available large language models (LLMs) to elucidate the SAR. Some LLMs, particularly Grok-3 Think and ChatGPT o1, outperform the traditional ML classifiers, and both approaches highlight positive charges and lipophilicity as key properties for effective cyanine antibiotics.

The blood–brain barrier (BBB) is a protective structure that controls which molecules can move from the bloodstream into the brain. While it helps keep the brain’s environment stable, it also makes it difficult to develop drugs that need to reach the brain. Traditionally, scientists have used many cell experiments and animal tests to figure out whether a drug can cross the BBB, which is costly and time-consuming. To address this, researchers are increasingly using advanced computer methods that analyze existing data to predict BBB permeability. With the growth of machine learning and deep learning, many new models have been created to improve these predictions. Accurately predicting whether small molecules can cross the BBB is crucial for modern drug discovery. Machine learning models make these predictions faster and more accurate, helping scientists identify which compounds are most likely to reach the human brain. Predicting whether small molecules can pass through the blood–brain barrier (BBB) is important for discovering new drugs. Artificial intelligence tools, such as machine learning and large language models, can make this process faster and more accurate. The main goal of this research is to create new AI models and deep learning methods that can accurately predict if a molecule can cross the human BBB.

The rapid growth of IoT has increased security vulnerabilities, especially from weak passwords. This study aims to develop and validate a machine learning tool to predict password vulnerabilities in smart home IoT devices and provide dynamic recommendations using a Large Language Model (LLM). The research addresses gaps in existing security measures by offering a data-driven model that predicts vulnerabilities and provides real-time, tailored recommendations. Archival data from previous IoT security research, including password cracking attempts, were used to train the model. Testing involved real-world password data and adversarial scenarios, with performance evaluated using accuracy, precision, recall, and F1-score. The findings show significant improvements in recall and F1-score with the Retrieval Augmented Generation (RAG) architecture compared to the baseline, suggesting RAG’s potential in enhancing IoT security. Organizations can use this model to improve their infrastructure’s security, reducing risks from weak passwords.

Database knob tuning is a long-standing challenge in the database community, aimed at enhancing the performance of database management systems (DBMSs) by minimizing latency and maximizing throughput. Manual tuning, which relies heavily on human expertise, is often inefficient and impractical for large-scale or dynamic deployments. Recent work has explored automating this process using machine learning (ML) and large language models (LLMs). However, existing methods typically require hundreds of workload replays or rely on extensive training data, leading to low tuning efficiency or high preparation costs. Moreover, they also risk generating invalid configurations that can degrade performance or even crash the database. To address these limitations, we introduce AgentTune, the first agent-based knob tuning framework powered by LLMs, designed for efficiency, adaptability, and reliability. AgentTune decomposes the tuning process into four specialized agents: Workload Analyzer, Knob Selector, Range Pruner, and Configuration Recommender, each responsible for a distinct subtask. These agents collaborate through structured prompt chaining. AgentTune first analyzes the input workload to identify impactful knobs and reconstructs their valid ranges to reduce the search space. It then employs a tree-based search strategy to efficiently explore the configuration space and identify suitable knob values. We conduct extensive evaluations across diverse workloads (public benchmarks and real-world workloads), metrics (latency and throughput), DBMSs (PostgreSQL, MySQL, and TiDB), hardware environments, and database scales. Experimental results demonstrate that, compared to existing baselines, AgentTune is able to identify superior configurations using significantly fewer workload replays. Furthermore, AgentTune rarely generates invalid configurations during the tuning process, ensuring reliability and suitability for real-world deployments.